PV temporal output shaping - visualization

Instructions

  • Make sure to update the revmpath, datapath, apikeys, and nsrdbparams variables in settings.py. This notebook makes extensive use of the plots.py module within pvvm.
In [1]:
### Standard imports
import pandas as pd
import numpy as np
import os, sys, site, pickle, json, shapely, zipfile
import matplotlib.pyplot as plt
import matplotlib as mpl
from glob import glob
from tqdm import tqdm, trange, tqdm_notebook, tnrange
import urllib, requests
import geopandas as gpd
import pvlib
from IPython.display import display, HTML

from matplotlib.ticker import (
    AutoMinorLocator, MultipleLocator, AutoLocator, PercentFormatter)
pd.options.display.max_columns = 200
pd.options.display.max_rows = 15
%config InlineBackend.figure_format = 'retina'

### Import the package
### (Note: scriptpath should lead to folder above 'pvvm' folder)
# scriptpath = os.path.expanduser('~/path/to/folder/above/pvvm/')
scriptpath = os.path.expanduser('~/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/')
site.addsitedir(scriptpath)
import pvvm

revmpath = pvvm.settings.revmpath
datapath = pvvm.settings.datapath
pvvm.plots.plotparams()

Import results

In [2]:
### Load TMY-optimized orientations
dfin = pd.read_csv(revmpath+'out/results_pvtos_tmyopt_public.csv.gz')
for col in ['ISOwecc','ISO:Node']:
    dfin[col] = dfin[col].astype('category')
dfin['yearsun'] = dfin['yearsun'].map(lambda x: 'tmy' if x == 'tmy' else int(float(x))).copy()
### Drop the Canada nodes
dfin = dfin.loc[~(dfin['ISO:Node'].isin(['MISO:MHEB','MISO:SPC']))].reset_index(drop=True)
    
### Load final revenue and CF results calculated under historical irradiance
dfin_optorient_histsun = pd.read_csv(revmpath+'out/results_pvtos_histsun_public.csv.gz')
for col in ['ISOwecc','ISO:Node']:
    dfin_optorient_histsun[col] = dfin_optorient_histsun[col].astype('category')
dfin_optorient_histsun['yearsun'] = dfin_optorient_histsun['yearsun'].map(
    lambda x: 'tmy' if x == 'tmy' else int(float(x))).copy()
### Drop the Canada nodes
dfin_optorient_histsun = dfin_optorient_histsun.loc[
    ~(dfin_optorient_histsun['ISO:Node'].isin(['MISO:MHEB','MISO:SPC']))].reset_index(drop=True)
/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (12,38,39,41,45,46,51,55,56,57,61) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (2,3,6,31) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

Shared data and figure properties

In [3]:
isos = ['CAISO','ERCOT','MISO','PJM','NYISO','ISONE']
colors = dict(zip(isos, ['C{}'.format(i) for i in range(len(isos))]))
alpha = 0.2
years = list(range(2010,2018))
mc = {'tmy':'k','da':'C0', 'rt':'C3'}
bootstrap = 5 ### Use 5 for testing and 5000 for publication
figheight = 12

innerband = ['25%','75%']
outerband = ['2.5%','97.5%']
percentiles = innerband + ['50%'] + outerband
fractions = [float(pct[:pct.find('%')])*.01 for pct in percentiles]
labels = {
    'outer': 'central {:.0f}%'.format((fractions[-1]-fractions[-2])*100),
    'inner': 'central {:.0f}%'.format((fractions[1]-fractions[0])*100),
    'median': 'median',
    'min': 'min & max',
    'max': '_nolabel_',
    'minmax': 'all nodes',
    'minmax': 'min & max',
}

Extract and calculate quantities of interest

In [4]:
################# All together
######### Assemble plot data
mergecols = ['ISOwecc','ISO:Node','yearlmp']
###### Fixed DA: optrev_rev vs optcf_rev; orientations
program = 'PVvalueOptV4'
systemtype = 'fixed'
yearsun = 'tmy'

###### Must-run
pricecutoff = 'no'
dfplot = dfin.loc[
    (dfin.market == 'da')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Rev/OptCF_Rev','OptRev_Azimuth','OptRev_Tilt',
             'OptRev_CF/OptCF_CF', 'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfrt = dfin.loc[
    (dfin.market == 'rt')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Rev/OptCF_Rev','OptRev_Azimuth','OptRev_Tilt',
             'OptRev_CF/OptCF_CF', 'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfplot = dfplot.merge(dfrt, on=mergecols, suffixes=('(da)','(rt)'), how='outer')

###### Curtailable
pricecutoff = '0'
dfcurtail = dfin.loc[
    (dfin.market == 'da')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Azimuth','OptRev_Tilt',
             'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfrt = dfin.loc[
    (dfin.market == 'rt')
    & (dfin.yearlmp.isin(years))
    & (dfin.program == program)
    & (dfin.systemtype == systemtype)
    & (dfin.yearsun == yearsun)
    & (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Azimuth','OptRev_Tilt',
             'OptCF_Azimuth','OptCF_Tilt',
             'OptCF_Rev','OptRev_Rev',
            ]].copy()

dfcurtail = dfcurtail.merge(dfrt, on=mergecols, suffixes=('(da)','(rt)'))

dfplot = dfplot.merge(dfcurtail, on=mergecols, suffixes=('(mustrun)','(curtail)'))
In [5]:
###### Track, DA and RT: Rev_curtail/Rev_mustrun

program = 'PVvalueV8'
pricecutoff = '0'
systemtype = 'track'

##########
### Ratios
datum = 'Rev_curtail/Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] / dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail/CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] / dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

###############
### Differences
datum = 'Rev_curtail-Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] - dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail-CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == program)
        & (dfin.systemtype == systemtype)
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] - dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')
    
# dfplot = dfplot.dropna(thresh=10).copy()
In [6]:
###############################
######### Historical irradiance
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor',
        'Revenue_dispatched','CapacityFactor_dispatched',
        'ValueAverage_dispatched','ValueFactor_dispatched',]
dicthist = {}
for market in ['da','rt']:
    ############ Revenue and CF ratios, fixed-tilt 
    ###### Must-run
    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue','CapacityFactor','ValueAverage','ValueFactor']:
        dicthist['{}_hist_optcf_f({})(mustrun)'.format(datum,market)] = df[datum]

    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == '0'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue','CapacityFactor','ValueAverage','ValueFactor']:
        dicthist['{}_hist_optrev_f({})(mustrun)'.format(datum,market)] = df[datum]
        
    ###### Curtailable
    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == 'opt0cutoff'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue_dispatched','CapacityFactor_dispatched',
                  'ValueAverage_dispatched','ValueFactor_dispatched']:
        dicthist['{}_hist_optcf_f({})(curtail)'.format(datum,market)] = df[datum]

    df = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == 'opt0cutoff'),
        ['ISO:Node','yearlmp']+data,
    ].sort_values(['yearlmp','ISO:Node'])
    df.index = df[['ISO:Node','yearlmp']]
    for datum in ['Revenue_dispatched','CapacityFactor_dispatched',
                  'ValueAverage_dispatched','ValueFactor_dispatched']:
        dicthist['{}_hist_optrev_f({})(curtail)'.format(datum,market)] = df[datum]

############ Tracking vs fixed CFopt, DA, must-run
######### must-run
for market in ['da', 'rt']:
    dftop = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == 'PVvalueV8')
        & (dfin.systemtype == 'track')
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dftop.index = dftop[['ISO:Node','yearlmp']]

    dfbot = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')#,
        & (dfin_optorient_histsun.mod1 == '0')
    #     ['ISO:Node','yearlmp','Revenue','CapacityFactor']
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dfbot.index = dfbot[['ISO:Node','yearlmp']]

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')
    dicthist['Revenue_hist_default_t({})(mustrun)'.format(market)] = dftop['Revenue']
    dicthist['Revenue_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['Revenue'] / dfbot['Revenue'])
    dicthist['CapacityFactor_hist_default_t({})(mustrun)'.format(market)] = dftop['CapacityFactor']
    dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['CapacityFactor'] / dfbot['CapacityFactor'])
    dicthist['Value_hist_default_t({})(mustrun)'.format(market)] = dftop['ValueAverage']
    dicthist['Value_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['ValueAverage'] / dfbot['ValueAverage'])
    dicthist['ValueFactor_hist_default_t({})(mustrun)'.format(market)] = dftop['ValueFactor']
    ###############
    ### Differences
    dicthist['Revenue_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['Revenue'] - dfbot['Revenue'])
    dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['CapacityFactor'] - dfbot['CapacityFactor'])
    dicthist['Value_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
        dftop['ValueAverage'] - dfbot['ValueAverage'])
    

######### curtailable
for market in ['da', 'rt']:
    dftop = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == 'PVvalueV8')
        & (dfin.systemtype == 'track')
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp')
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dftop.index = dftop[['ISO:Node','yearlmp']]

    dfbot = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')#,
        & (dfin_optorient_histsun.mod1 == 'opt0cutoff')
    #     ['ISO:Node','yearlmp','Revenue','CapacityFactor']
    ].sort_values(['yearlmp','ISO:Node']).copy()
    dfbot.index = dfbot[['ISO:Node','yearlmp']]

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')
    ###### Baseline = curtailable
    dicthist['Revenue_hist_default_t({})(curtail)'.format(market)] = dftop['Revenue_dispatched']
    dicthist['CapacityFactor_hist_default_t({})(curtail)'.format(market)] = dftop['CapacityFactor_dispatched']
    dicthist['Revenue_track(def)/fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['Revenue_dispatched'] / dfbot['Revenue_dispatched'])
    dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['CapacityFactor_dispatched'] / dfbot['CapacityFactor_dispatched'])
    dicthist['Value_hist_default_t({})(curtail)'.format(market)] = dftop['ValueAverage_dispatched']
    dicthist['ValueFactor_hist_default_t({})(curtail)'.format(market)] = dftop['ValueFactor_dispatched']
    ###### Baseline = mustrun
    dicthist['Revenue_track(def)/fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['Revenue_dispatched'] / dfbot['Revenue']) ### CHANGED dfbot from _Dispatched
    dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['CapacityFactor_dispatched'] / dfbot['CapacityFactor']) ### CHANGED dfbot from _Dispatched
    ###############
    ### Differences
    dicthist['Revenue_track(def)-fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['Revenue_dispatched'] - dfbot['Revenue_dispatched'])
    dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
        dftop['CapacityFactor_dispatched'] - dfbot['CapacityFactor_dispatched'])
    dicthist['Revenue_track(def)-fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['Revenue_dispatched'] - dfbot['Revenue']) ### CHANGED dfbot from _Dispatched
    dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
        dftop['CapacityFactor_dispatched'] - dfbot['CapacityFactor']) ### CHANGED dfbot from _Dispatched

### Merge them
dfmerge = pd.concat(dicthist,axis=1)

for market in ['da','rt']:
    ###### Take ratios
    ##### Must-run
    dfmerge['Rev_OptRev/OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['Revenue_hist_optrev_f({})(mustrun)'.format(market)] 
        / dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)])
    dfmerge['CF_OptRev/OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['CapacityFactor_hist_optrev_f({})(mustrun)'.format(market)] 
        / dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
    ##### Curtailable
    ### Baseline = curtailable
    dfmerge['Rev_OptRev/OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['Revenue_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    dfmerge['CF_OptRev/OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['CapacityFactor_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    ### Baseline = mustrun
    dfmerge['Rev_OptRev/OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)]) ### CHANGED from _Dispatched, curtail
    dfmerge['CF_OptRev/OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        / dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)]) ### CHANGED from _Dispatched,curtail
    ###############
    ### Differences
    ##### Must-run
    dfmerge['Rev_OptRev-OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['Revenue_hist_optrev_f({})(mustrun)'.format(market)] 
        - dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)])
    dfmerge['CF_OptRev-OptCF_hist,{},f,mustrun'.format(market)] = (
        dfmerge['CapacityFactor_hist_optrev_f({})(mustrun)'.format(market)] 
        - dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
    ##### Curtailable
    ### Baseline = curtailable
    dfmerge['Rev_OptRev-OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['Revenue_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    dfmerge['CF_OptRev-OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['CapacityFactor_dispatched_hist_optcf_f({})(curtail)'.format(market)])
    ### Baseline = mustrun
    dfmerge['Rev_OptRev-OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)]) 
    dfmerge['CF_OptRev-OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
        dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)] 
        - dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
    

dfmerge.index = pd.MultiIndex.from_tuples(dfmerge.index, names=('ISO:Node', 'yearlmp'))
dfmerge.reset_index(inplace=True)

dfplot = dfplot.merge(dfmerge, on=['ISO:Node','yearlmp'])
/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:156: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

In [8]:
###### Fixed, DA and RT: Rev_curtail/Rev_mustrun

pricecutoff = '0'
systemtype = 'fixed'

##########
### Ratios
datum = 'Rev_curtail/Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] / dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail/CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] / dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')
    
###############
### Differences
datum = 'Rev_curtail-Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['Revenue_dispatched'] - dfmerge['Revenue'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')

datum = 'CF_curtail-CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
    dfmerge = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == systemtype)
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0')
    ].copy()
    ### Get ratio
    dfmerge[datum+'({})'.format(market)] = (
        dfmerge['CapacityFactor_dispatched'] - dfmerge['CapacityFactor'])
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[datum+'({})'.format(market)]], 
        on=mergecols, how='outer')
In [9]:
############ Azimuth orientation for 1-axis-tracking sytems, vs CF-optimized fixed-tilt must-run
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor']
###### Must-run
for market in ['da','rt']:
    dftop = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == 'track')
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == 'azopt'),
        mergecols+data
    ].copy()


    dfbot = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.systemtype == 'fixed')
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.mod0 == 'optcf')
        & (dfin_optorient_histsun.mod1 == '0'),
        mergecols+data
    ].copy()

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')

    dfmerge = dftop.merge(
        dfbot, on=['ISOwecc','ISO:Node','yearlmp'], 
        suffixes=('_track,optrev,{},mustrun'.format(market), '_fixed,optcf,{},mustrun'.format(market)),
    )

    ### Calculate ratios
    dfmerge['CF_track,optrev,{},mustrun/CF_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        / dfmerge['CapacityFactor_fixed,optcf,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun/Rev_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        / dfmerge['Revenue_fixed,optcf,{},mustrun'.format(market)]
    )
    ###############
    ### Differences
    dfmerge['CF_track,optrev,{},mustrun-CF_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        - dfmerge['CapacityFactor_fixed,optcf,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun-Rev_fixed,optcf,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        - dfmerge['Revenue_fixed,optcf,{},mustrun'.format(market)]
    )
    
    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[
            'CF_track,optrev,{},mustrun/CF_fixed,optcf,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun/Rev_fixed,optcf,{},mustrun'.format(market,market),
            'CF_track,optrev,{},mustrun-CF_fixed,optcf,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun-Rev_fixed,optcf,{},mustrun'.format(market,market),
        ]], 
        on=mergecols, how='outer',
    )
In [10]:
############ Azimuth orientation for 1-axis-tracking sytems, vs default track must-run
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor']
###### Must-run
for market in ['da','rt']:
    dftop = dfin_optorient_histsun.loc[
        (dfin_optorient_histsun.market == market)
        & (dfin_optorient_histsun.yearlmp.isin(years))
        & (dfin_optorient_histsun.systemtype == 'track')
        & (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
        & (dfin_optorient_histsun.Product == 'lmp')
        & (dfin_optorient_histsun.mod0 == 'optrev')
        & (dfin_optorient_histsun.mod1 == 'azopt'),
        mergecols+data
    ].copy()

    dfbot = dfin.loc[
        (dfin.market == market)
        & (dfin.yearlmp.isin(years))
        & (dfin.program == 'PVvalueV8')
        & (dfin.systemtype == 'track')
        & (dfin.yearsun == dfin.yearlmp)
        & (dfin.Product == 'lmp'),
        mergecols+data
    ].copy()

    if len(dftop) != len(dfbot):
        print(market)
        print('len(dftop) = {}'.format(len(dftop)))
        print('len(dfbot) = {}'.format(len(dfbot)))
        raise Exception('unequal lengths')

    dfmerge = dftop.merge(
        dfbot, on=['ISOwecc','ISO:Node','yearlmp'], 
        suffixes=('_track,optrev,{},mustrun'.format(market), '_track,default,{},mustrun'.format(market)),
    )

    ### Calculate ratios
    dfmerge['CF_track,optrev,{},mustrun/CF_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        / dfmerge['CapacityFactor_track,default,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun/Rev_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        / dfmerge['Revenue_track,default,{},mustrun'.format(market)]
    )
    ###############
    ### Differences
    dfmerge['CF_track,optrev,{},mustrun-CF_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
        - dfmerge['CapacityFactor_track,default,{},mustrun'.format(market)]
    )
    dfmerge['Rev_track,optrev,{},mustrun-Rev_track,default,{},mustrun'.format(market,market)] = (
        dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
        - dfmerge['Revenue_track,default,{},mustrun'.format(market)]
    )

    ### Merge with rest of plot data
    dfplot = dfplot.merge(
        dfmerge[mergecols+[
            'CF_track,optrev,{},mustrun/CF_track,default,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun/Rev_track,default,{},mustrun'.format(market,market),
            'CF_track,optrev,{},mustrun-CF_track,default,{},mustrun'.format(market,market),
            'Rev_track,optrev,{},mustrun-Rev_track,default,{},mustrun'.format(market,market),
        ]], 
        on=mergecols, how='outer',
    )
In [11]:
####### Azimuth optimization for tracking systems
systemtype = 'track'
pricecutoff = 'no'
yearsun = 'tmy'
program = 'PVvalueOptV4'

dfmerge = dfin.loc[
    (dfin.program==program)
    &(dfin.systemtype==systemtype)
    &(dfin.pricecutoff==pricecutoff)
    &(dfin.yearsun==yearsun)
    &(dfin.market=='da'),
    mergecols+['OptCF_Azimuth']
].rename(columns={'OptCF_Azimuth':'OptCF_Azimuth(track)'})

for market in ['da','rt']:
    dfmerge = dfmerge.merge(
        dfin.loc[
            (dfin.program==program)
            &(dfin.systemtype==systemtype)
            &(dfin.pricecutoff==pricecutoff)
            &(dfin.yearsun==yearsun)
            &(dfin.market==market),
            mergecols+['OptRev_Azimuth']
        ].rename(columns={'OptRev_Azimuth':'OptRev_Azimuth(track)({})(mustrun)'.format(market)}),
        on=mergecols, how='outer')

for datum in ['OptCF_Azimuth(track)', 'OptRev_Azimuth(track)(da)(mustrun)', 
              'OptRev_Azimuth(track)(rt)(mustrun)', ]:
    dfmerge[datum] = dfmerge[datum].map(lambda x: x + 180 if x < 90 else x)
    
dfplot = dfplot.merge(dfmerge, on=mergecols, how='outer').copy()

Figures

Baseline results

In [12]:
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CapacityFactor_hist_optcf_f(da)(mustrun)',
    'Revenue_hist_optcf_f(da)(mustrun)',
    'Revenue_hist_optcf_f(rt)(mustrun)',
    'ValueAverage_hist_optcf_f(da)(mustrun)',
    'ValueAverage_hist_optcf_f(rt)(mustrun)',
    'ValueFactor_hist_optcf_f(da)(mustrun)',
    'ValueFactor_hist_optcf_f(rt)(mustrun)',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))


### Column-indexed parameters
ncols = 4
# cols = [0,1,2,3]
ylim = [
    [0,0.3],
    [0,180], #[0,210], 
    [0,90], #[0,110],
    [0.5,1.7], #[0.6,1.8],
]

ylabel = [
    'CF',
    'Revenue',
    'Value',
    'VF',
]

note = [
    '[fraction]',
    '[$/kWac-yr]',
    '[$/MWh]',
    '[fraction]',
]

gridspec_kw = {'width_ratios': [1, 2, 2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            # medianmarker='_', mediansize=10, medianfacecolor='k'
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=1.2, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ### Add 1-line for VF
        ax[(row,-1)].axhline(1, lw=0.25, c='0.5')
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(0,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Quantities, CF-optimized fixed-tilt, must-run', weight='bold', y=1.07, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
In [13]:
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
CapacityFactor_hist_optcf_f(da)(mustrun) Revenue_hist_optcf_f(da)(mustrun) Revenue_hist_optcf_f(rt)(mustrun) ValueAverage_hist_optcf_f(da)(mustrun) ValueAverage_hist_optcf_f(rt)(mustrun) ValueFactor_hist_optcf_f(da)(mustrun) ValueFactor_hist_optcf_f(rt)(mustrun)
count 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000
mean 0.246188 59.642007 53.883991 27.708089 25.040576 0.801583 0.788523
std 0.011704 6.841004 7.212112 3.342419 3.514949 0.062013 0.079220
min 0.187778 28.469182 27.826508 12.993617 13.054663 0.451965 0.469798
2.5% 0.225073 45.610871 40.140121 20.772181 18.265009 0.671348 0.639226
25% 0.240347 56.143990 49.279463 25.543496 22.333303 0.761790 0.728410
50% 0.245543 59.215096 52.814589 27.703784 25.011672 0.816433 0.811701
75% 0.252588 63.347934 58.849791 29.722195 27.819860 0.843112 0.851000
97.5% 0.274404 75.239969 69.971012 34.382648 31.960051 0.915864 0.905468
max 0.282761 82.950998 74.620503 40.043755 35.075066 0.976529 0.937086
median
CapacityFactor_hist_optcf_f(da)(mustrun) Revenue_hist_optcf_f(da)(mustrun) Revenue_hist_optcf_f(rt)(mustrun) ValueAverage_hist_optcf_f(da)(mustrun) ValueAverage_hist_optcf_f(rt)(mustrun) ValueFactor_hist_optcf_f(da)(mustrun) ValueFactor_hist_optcf_f(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 0.230972 NaN 0.193482 0.198847 0.194502 0.198551 NaN 91.630257 NaN 105.497863 77.500738 112.758798 91.734709 NaN 94.208201 NaN NaN 74.620116 117.453727 90.926518 NaN 45.325221 NaN 62.599238 43.293228 66.853404 54.546457 NaN 46.945908 NaN NaN 41.735509 67.335810 53.880279 NaN 1.109605 NaN 1.138015 1.219608 1.152851 1.165117 NaN 1.062646 NaN NaN 1.222089 1.144497 1.177101 NaN
2011 0.243936 0.234866 0.190098 0.189981 0.189608 0.188582 NaN 86.103130 163.255041 94.790453 69.637654 102.834552 89.623317 NaN 80.671016 147.202962 95.315083 67.368666 102.822357 90.472253 NaN 40.417082 79.033268 56.953720 41.132766 61.773388 54.993100 NaN 37.881238 71.076056 57.200568 39.981424 62.023122 56.037231 NaN 1.193562 1.591313 1.128503 1.214730 1.150326 1.207836 NaN 1.120612 1.533552 1.126617 1.212205 1.177433 1.224964 NaN
2012 0.244936 0.232577 0.199708 0.210066 0.196966 0.203782 NaN 76.214457 82.804797 75.495416 60.433441 79.442607 70.136512 NaN 76.621147 70.834097 75.994444 59.939321 81.261827 71.671996 NaN 35.591168 40.427457 43.116905 33.426375 46.395278 40.112320 NaN 35.546166 34.519891 43.572244 33.230227 47.427459 40.858529 NaN 1.139462 1.376841 1.115548 1.175683 1.150527 1.165354 NaN 1.145248 1.290854 1.128399 1.190303 1.184282 1.177129 NaN
2013 0.258688 0.226295 0.198371 0.195050 0.195548 0.194807 NaN 107.022227 79.332946 109.051458 61.371157 102.792563 72.538165 NaN 98.699543 74.592091 108.962743 60.310346 103.703793 73.717128 NaN 47.221461 39.773405 63.002813 36.261442 60.355995 44.514600 NaN 43.754006 37.518670 62.770983 35.717012 61.095318 44.325946 NaN 1.059504 1.182064 1.077531 1.137146 1.133439 1.153043 NaN 1.041000 1.160513 1.077664 1.135742 1.166363 1.164494 NaN
2014 0.245462 0.222564 0.194251 0.204298 0.189929 0.193770 NaN 108.886657 88.954743 119.572237 76.334670 113.466331 87.085898 NaN 99.847372 81.070067 111.806277 72.871556 108.451109 87.549573 NaN 50.624526 45.230950 70.751555 43.106767 68.232393 52.933508 NaN 47.074163 41.357884 66.180022 41.062071 66.128568 53.510857 NaN 1.022801 1.137982 1.069726 1.101950 1.082699 1.086669 NaN 0.992912 1.087337 1.027523 1.069602 1.110393 1.110727 NaN
2015 0.248991 0.213987 0.201143 0.203341 0.199722 0.196122 0.226895 73.239214 67.027475 81.934695 55.204096 79.654977 67.236213 60.160760 70.537787 53.928449 77.476387 54.091967 76.588783 64.386584 45.203972 33.848354 35.470877 46.708785 31.837814 45.406957 39.430827 30.262247 33.193210 28.553451 44.117595 30.718069 43.297521 37.790873 23.187285 0.993296 1.343341 1.087579 1.146485 1.152415 1.164359 0.955955 0.976788 1.196270 1.054393 1.156216 1.129825 1.144031 1.069051
2016 0.248407 0.227279 0.209589 0.212410 0.206703 0.201994 0.233462 59.331690 59.706667 61.372094 55.933880 61.697934 57.699422 50.692495 55.657016 55.293798 57.768955 54.724383 59.509091 57.117897 40.173297 27.562865 29.540111 33.328241 30.620093 33.805654 33.158103 24.467022 25.910069 27.542636 31.430099 29.891158 32.537043 32.603484 19.214541 0.919551 1.311012 1.099718 1.173928 1.157107 1.169964 0.872516 0.870209 1.276788 1.071098 1.177482 1.164329 1.165288 0.850056
2017 0.245543 0.226272 0.199129 0.208934 0.197039 0.199729 0.254154 59.215096 58.688937 60.681659 57.310864 60.449408 57.115360 50.727470 52.814589 55.402269 57.717520 57.560303 56.736561 57.307566 37.422478 27.703784 29.010892 34.789771 32.132992 35.720484 33.341268 24.026835 25.011672 27.621548 33.125203 32.137731 33.042092 33.385809 19.410569 0.816433 1.232272 1.048331 1.173494 1.118062 1.137926 0.754621 0.811701 1.184371 0.988141 1.191962 1.051551 1.144193 0.741065
max
CapacityFactor_hist_optcf_f(da)(mustrun) Revenue_hist_optcf_f(da)(mustrun) Revenue_hist_optcf_f(rt)(mustrun) ValueAverage_hist_optcf_f(da)(mustrun) ValueAverage_hist_optcf_f(rt)(mustrun) ValueFactor_hist_optcf_f(da)(mustrun) ValueFactor_hist_optcf_f(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 0.277866 NaN 0.207146 0.225963 0.206615 0.228378 NaN 116.851289 NaN 117.537375 113.244225 162.037417 174.184442 NaN 130.018708 NaN NaN 114.945681 160.141739 164.998095 NaN 51.138572 NaN 68.874514 70.606498 91.530305 90.246885 NaN 56.869385 NaN NaN 71.667337 89.056471 85.487338 NaN 1.139584 NaN 1.153784 1.304819 1.198685 1.357076 NaN 1.179287 NaN NaN 1.370531 1.210511 1.333850 NaN
2011 0.285192 0.279049 0.205588 0.223398 0.206670 0.222986 NaN 106.351969 426.367353 105.779767 89.751867 145.127694 152.744892 NaN 99.718389 326.267108 108.772527 87.924704 153.641869 142.928318 NaN 46.800609 180.349037 61.840130 56.492077 83.694936 80.060863 NaN 44.866865 138.007656 61.772892 55.342015 87.563832 74.915530 NaN 1.253313 1.779045 1.147259 1.301418 1.263254 1.368157 NaN 1.237704 1.792053 1.155443 1.381830 1.289410 1.360069 NaN
2012 0.281405 0.273209 0.207713 0.231437 0.208879 0.218694 NaN 120.540637 778.858911 87.800296 74.783118 122.350396 137.166419 NaN 120.537862 694.822127 91.392987 82.178646 123.838466 138.588866 NaN 54.646675 336.886631 49.998567 47.209960 66.694580 71.803614 NaN 54.572420 300.006977 51.676310 51.878695 68.664584 72.548234 NaN 1.367523 1.940491 1.173878 1.326116 1.341058 1.272452 NaN 1.404304 2.121927 1.213145 1.361444 1.310590 1.285923 NaN
2013 0.283820 0.269719 0.206275 0.217156 0.210662 0.214248 NaN 126.315515 295.869659 124.496771 78.324788 156.028937 122.917740 NaN 115.165879 219.394761 121.951244 79.830470 204.811975 125.289742 NaN 54.949088 128.588504 69.726218 46.239649 86.942269 67.635080 NaN 51.113866 95.318513 68.044938 46.308729 114.125099 68.940266 NaN 1.131657 1.813841 1.130427 1.253033 1.191331 1.282994 NaN 1.122203 1.701891 1.096285 1.282900 1.274053 1.364994 NaN
2014 0.279916 0.265088 0.203394 0.223683 0.206444 0.215460 NaN 143.808370 292.514372 127.472113 114.593187 144.684377 193.165455 NaN 132.967096 197.115729 120.849700 114.276118 161.456743 187.543788 NaN 65.550925 137.372822 75.338511 64.177441 84.260185 104.543246 NaN 61.328208 89.325593 70.148506 63.999868 91.952168 101.926090 NaN 1.075579 1.664182 1.153818 1.249215 1.131118 1.167482 NaN 1.079433 1.532755 1.128290 1.252162 1.173316 1.314645 NaN
2015 0.280158 0.255952 0.209564 0.219845 0.211239 0.211704 0.260411 111.083014 104.810763 101.922449 71.453293 109.363575 132.634029 68.143543 110.014158 87.213937 83.699451 70.988370 105.433792 128.250100 52.701057 51.850500 49.667198 56.960907 39.863414 61.837104 72.657013 35.123601 50.599382 48.368216 48.159494 40.116250 63.448290 70.404004 25.880763 1.085500 1.534029 1.202821 1.244111 1.305214 1.302858 1.045935 1.114281 1.528232 1.109420 1.328237 1.516640 1.457411 1.142447
2016 0.281632 0.262511 0.219156 0.231624 0.222363 0.225568 0.276496 67.441523 128.198562 66.022374 73.002232 91.640258 120.496917 62.088274 64.641734 141.959905 62.485980 72.925825 98.416609 111.255032 58.409003 33.016643 65.213393 35.491711 39.262600 52.523487 62.346818 28.823508 33.178101 72.213658 33.983111 39.221507 56.407343 57.564935 26.436307 1.000511 1.727105 1.131670 1.266791 1.626424 1.300367 0.980711 0.971997 1.796781 1.214950 1.365266 1.703000 1.290563 1.045332
2017 0.282761 0.267850 0.207351 0.227381 0.209204 0.226219 0.276142 82.950998 104.163372 64.060605 82.061544 86.033215 87.835021 63.711292 74.620503 128.990140 62.265714 86.511683 77.479656 80.494622 60.736481 40.043755 52.523915 37.047420 43.651335 47.783136 47.560762 32.996176 35.075066 63.937091 35.346495 44.407004 43.032461 47.245435 28.116618 0.976529 1.507225 1.207329 1.310353 1.405340 1.286612 0.940674 0.937086 1.620960 1.482201 1.371905 1.447970 1.287909 1.073048
CapacityFactor_hist_optcf_f(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.232652 0.245190 0.244435 0.258043 0.246584 0.248396 0.247330 0.246188 0.237353 0.233811 0.229137 0.224395 0.216660 0.229445 0.228360 0.190555 0.188857 0.197713 0.195224 0.191377 0.199974 0.207266 0.197753 0.197341 0.190831 0.207693 0.194743 0.202065 0.201792 0.209760 0.207154 0.189120 0.186542 0.193139 0.189843 0.187356 0.196382 0.203475 0.193788 0.198312 0.190271 0.202780 0.193415 0.193326 0.195856 0.203491 0.199148 0.221632 0.236713 0.238426
std 0.014412 0.012606 0.012300 0.008757 0.012197 0.009014 0.012372 0.011704 0.010747 0.010504 0.012283 0.011707 0.010886 0.009475 0.010145 0.009408 0.006664 0.005534 0.008408 0.007473 0.004207 0.006670 0.005155 0.009304 0.009098 0.010889 0.011114 0.012111 0.007098 0.010169 0.008433 0.014195 0.012401 0.008876 0.013602 0.010603 0.008383 0.009424 0.009766 0.010938 0.012499 0.007841 0.010048 0.009472 0.006770 0.008252 0.008816 0.015364 0.024022 0.030500
min 0.173918 0.193692 0.171110 0.203060 0.195900 0.199669 0.196407 0.187778 0.219438 0.212187 0.206964 0.197210 0.198289 0.210270 0.208513 0.161775 0.168907 0.181200 0.169681 0.169265 0.185596 0.183795 0.178488 0.174960 0.170079 0.176083 0.165189 0.154668 0.176217 0.183681 0.175909 0.164429 0.163120 0.174881 0.161536 0.166203 0.178952 0.185901 0.173762 0.169249 0.162624 0.177008 0.160435 0.169993 0.177850 0.183756 0.175545 0.175029 0.160074 0.165343
2.5% 0.208335 0.222666 0.223061 0.238273 0.225968 0.234869 0.219041 0.225073 0.224218 0.217323 0.210992 0.204041 0.202336 0.215856 0.214852 0.168085 0.174743 0.185035 0.175755 0.174051 0.188857 0.191418 0.184829 0.180039 0.176544 0.183455 0.171857 0.176615 0.183624 0.189082 0.188677 0.166983 0.165511 0.177199 0.165342 0.170124 0.179592 0.186832 0.177004 0.178770 0.170649 0.185958 0.172550 0.174672 0.182475 0.189725 0.182668 0.184294 0.177503 0.177094
25% 0.224480 0.238225 0.239986 0.255004 0.238837 0.242646 0.240335 0.240347 0.231548 0.228076 0.222471 0.217984 0.210657 0.223966 0.222721 0.188044 0.185747 0.194775 0.191531 0.188172 0.198717 0.204839 0.196382 0.188936 0.185344 0.198671 0.186321 0.193984 0.199122 0.203121 0.202481 0.174682 0.173456 0.184431 0.176722 0.176092 0.187735 0.194185 0.183894 0.190996 0.181061 0.197812 0.187423 0.187713 0.190953 0.197538 0.193094 0.214574 0.223098 0.221056
50% 0.230972 0.243936 0.244936 0.258688 0.245462 0.248991 0.248407 0.245543 0.234866 0.232577 0.226295 0.222564 0.213987 0.227279 0.226272 0.193482 0.190098 0.199708 0.198371 0.194251 0.201143 0.209589 0.199129 0.198847 0.189981 0.210066 0.195050 0.204298 0.203341 0.212410 0.208934 0.194502 0.189608 0.196966 0.195548 0.189929 0.199722 0.206703 0.197039 0.198551 0.188582 0.203782 0.194807 0.193770 0.196122 0.201994 0.199729 0.226895 0.233462 0.254154
75% 0.241740 0.252387 0.249604 0.262092 0.255667 0.252410 0.255109 0.252588 0.239234 0.236262 0.231392 0.228012 0.218764 0.232245 0.230555 0.197123 0.193722 0.201651 0.201684 0.196809 0.202754 0.211578 0.201086 0.203396 0.195544 0.216550 0.204496 0.211216 0.206093 0.216798 0.213269 0.202244 0.198042 0.199148 0.201630 0.195815 0.202614 0.212031 0.202601 0.204765 0.197942 0.208652 0.201026 0.199638 0.201261 0.210079 0.204569 0.231159 0.260125 0.262199
97.5% 0.269139 0.277368 0.271449 0.275072 0.271443 0.269770 0.272408 0.274404 0.271558 0.264260 0.263887 0.256828 0.247668 0.257023 0.260103 0.201700 0.198671 0.204188 0.205412 0.200667 0.205269 0.215520 0.204218 0.212018 0.214101 0.223600 0.210027 0.218603 0.212175 0.224108 0.219534 0.206602 0.203847 0.206583 0.207266 0.204395 0.209293 0.215591 0.205754 0.221948 0.217520 0.215017 0.209066 0.210115 0.206933 0.219446 0.216040 0.243591 0.267128 0.269153
max 0.277866 0.285192 0.281405 0.283820 0.279916 0.280158 0.281632 0.282761 0.279049 0.273209 0.269719 0.265088 0.255952 0.262511 0.267850 0.207146 0.205588 0.207713 0.206275 0.203394 0.209564 0.219156 0.207351 0.225963 0.223398 0.231437 0.217156 0.223683 0.219845 0.231624 0.227381 0.206615 0.206670 0.208879 0.210662 0.206444 0.211239 0.222363 0.209204 0.228378 0.222986 0.218694 0.214248 0.215460 0.211704 0.225568 0.226219 0.260411 0.276496 0.276142
Revenue_hist_optcf_f(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 92.017992 86.390811 77.974030 107.271957 110.104298 73.928335 58.816583 59.642007 164.868145 91.047394 85.353546 92.596227 68.501629 60.761364 61.179336 104.678846 94.423800 75.294163 107.608886 118.502668 81.864221 60.588686 59.961438 75.715618 68.528698 60.194795 60.332129 76.962169 54.648883 56.121925 59.714467 111.509157 103.250748 81.746568 101.160582 106.310234 75.319010 60.498390 58.554497 99.902506 92.626753 72.116685 75.925413 92.390270 69.615453 60.004503 58.384859 59.665230 50.825820 50.237658
std 6.005148 5.572234 8.389878 6.446090 9.415272 7.362007 3.933406 6.841004 14.395855 39.251020 24.425163 16.505927 5.253532 4.789278 7.581061 6.892035 4.123885 3.448186 6.184023 5.252478 3.498104 2.915279 2.681080 7.830648 7.746373 6.569510 5.763363 9.851860 5.391374 6.321088 7.773952 27.356155 25.868541 18.001874 28.357297 20.671382 15.944648 13.746859 12.728755 19.817720 14.544858 6.797960 8.818024 14.604734 9.496760 7.983352 4.964799 2.986022 3.773570 3.695830
min 71.274534 61.611789 50.573090 84.319527 87.988728 53.915681 41.329243 28.469182 125.249880 74.047564 69.603852 74.660346 60.389228 52.059542 43.690718 83.379435 81.050723 66.660673 87.551765 100.408828 70.252272 48.377149 42.338154 53.946476 51.359806 44.469473 43.787464 43.895560 40.793988 36.350701 43.450319 73.250614 66.254367 58.395045 60.035540 58.002171 42.424838 34.917746 34.507602 54.537289 64.068315 55.295119 59.018591 63.240606 43.773042 38.406954 47.364189 49.927987 38.471109 41.306530
2.5% 79.290844 74.528975 63.876227 94.882491 94.949389 63.312943 51.505725 45.610871 150.893603 76.624588 74.536295 81.245414 63.339557 56.316918 53.613932 88.370756 85.970329 68.715172 95.341394 107.319311 73.688511 52.393110 52.678298 59.060405 52.324227 46.210260 47.576138 59.280506 44.501709 40.687911 46.665420 74.091354 68.585434 60.033661 61.755615 71.853762 49.204100 35.473203 35.440768 76.191814 74.133712 63.693213 65.506673 73.900233 58.292767 50.443675 50.657739 52.259418 41.771970 43.556548
25% 88.526639 83.447599 73.198409 102.891764 103.476367 68.846922 56.010668 56.143990 159.963083 81.285597 77.790668 86.510929 65.729020 58.362987 56.056007 101.603689 91.566926 73.214170 102.935325 116.177159 80.610123 59.169902 59.516981 71.755705 63.132385 55.691935 56.457682 71.328454 51.796658 52.512388 54.348788 80.625940 76.057109 64.589622 69.007232 82.451967 59.395270 48.659040 46.578631 81.776810 78.696750 67.415090 68.824927 80.576582 63.582569 55.694614 55.164823 58.272293 48.675114 47.887450
50% 91.630257 86.103130 76.214457 107.022227 108.886657 73.239214 59.331690 59.215096 163.255041 82.804797 79.332946 88.954743 67.027475 59.706667 58.688937 105.497863 94.790453 75.495416 109.051458 119.572237 81.934695 61.372094 60.681659 77.500738 69.637654 60.433441 61.371157 76.334670 55.204096 55.933880 57.310864 112.758798 102.834552 79.442607 102.792563 113.466331 79.654977 61.697934 60.449408 91.734709 89.623317 70.136512 72.538165 87.085898 67.236213 57.699422 57.115360 60.160760 50.692495 50.727470
75% 96.640049 90.556029 82.294617 112.096901 114.976130 77.235285 61.898480 63.347934 165.906664 84.229868 82.340444 91.402491 69.417171 62.070897 63.316588 109.781746 97.608945 77.325865 111.836139 122.245235 83.360243 62.435779 61.347449 80.095639 72.697239 64.499733 64.399260 81.505728 58.226821 60.867060 64.973072 135.822436 129.754458 95.809650 129.957604 119.131628 85.857255 69.880305 70.291425 118.716239 106.429179 75.719462 82.894914 103.521574 75.098884 63.759995 61.268355 61.179078 53.155048 51.937046
97.5% 103.933588 95.583254 97.323221 118.441945 132.925730 89.602914 64.740581 75.239969 207.276980 199.118649 176.967152 151.489954 82.306710 70.843367 82.599720 115.133447 101.320813 85.090230 117.149755 126.313890 86.609769 64.681776 62.560937 88.473722 82.241410 71.411044 68.937072 102.916788 63.650568 68.051423 73.901229 152.518865 139.632716 118.621598 152.911916 143.660622 106.731261 86.165339 81.044174 131.539968 115.334738 85.048185 94.474197 122.003194 91.682238 79.805848 67.431143 65.458598 58.714801 57.496046
max 116.851289 106.351969 120.540637 126.315515 143.808370 111.083014 67.441523 82.950998 426.367353 778.858911 295.869659 292.514372 104.810763 128.198562 104.163372 117.537375 105.779767 87.800296 124.496771 127.472113 101.922449 66.022374 64.060605 113.244225 89.751867 74.783118 78.324788 114.593187 71.453293 73.002232 82.061544 162.037417 145.127694 122.350396 156.028937 144.684377 109.363575 91.640258 86.033215 174.184442 152.744892 137.166419 122.917740 193.165455 132.634029 120.496917 87.835021 68.143543 62.088274 63.711292
Revenue_hist_optcf_f(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 97.383341 81.933103 80.399598 98.337395 101.255145 69.287040 54.830548 53.883991 149.192436 80.347571 79.848675 84.395666 55.213807 56.717095 58.282512 NaN 94.943911 75.989413 106.772965 110.967069 76.995658 57.049205 57.084254 73.260644 67.043250 59.917161 59.637142 73.590786 54.056863 55.260440 59.939343 113.628707 102.967354 82.285210 103.588846 103.571142 73.856640 60.016645 54.867060 100.114408 93.644976 73.500016 75.547609 91.632134 66.804731 58.730709 58.175067 44.451403 40.501281 39.456444
std 10.789130 7.673545 13.325080 6.537813 9.140125 10.637636 4.960570 7.212112 13.388035 44.873751 19.277648 14.512568 4.987191 5.904052 8.911151 NaN 4.537097 3.888703 7.163677 5.116133 2.570858 2.926616 3.077326 7.402907 7.063501 5.861208 6.579268 10.656896 6.660088 6.611914 8.587664 29.692648 24.522276 17.577749 32.072987 21.040395 15.916859 15.165319 12.147695 21.153357 15.321099 6.778444 9.007610 13.667067 9.047200 7.661092 4.619127 3.071947 7.284882 7.350931
min 51.070740 59.563879 45.561790 80.509229 79.236077 42.841082 33.778017 27.826508 129.058755 62.932544 66.772353 72.717154 47.472676 48.831916 35.322076 NaN 79.852823 65.503191 83.752796 94.736047 68.085368 46.584139 36.514438 52.146149 50.671697 40.998470 37.805957 34.903865 37.677918 34.943804 41.593879 73.566140 67.309719 58.707083 56.933855 54.156004 36.163124 31.323744 30.013405 46.328597 63.415392 59.622172 60.144651 59.167099 42.067349 37.751920 45.906879 35.683326 24.534421 16.487856
2.5% 79.962447 65.525861 58.694705 85.801711 88.097041 51.183557 45.594587 40.140121 135.169008 64.812043 68.982385 74.547617 50.434933 52.015167 49.336312 NaN 86.420153 69.091122 92.218752 100.464073 70.150784 48.937462 46.663027 58.478083 53.508832 48.782820 46.243968 55.202733 42.083453 40.656617 46.746613 74.615701 70.260115 60.910039 59.264636 69.096340 46.027522 32.970313 31.845105 75.009021 73.525858 65.161414 64.485819 74.265051 56.345561 48.696140 50.873202 36.744722 27.140630 30.513905
25% 90.474501 77.541906 71.777296 94.168444 95.138763 59.903849 51.726545 49.279463 143.930722 68.742494 72.929978 78.893075 52.787009 54.072354 52.738248 NaN 91.616091 73.153077 101.608385 108.167476 75.569785 55.679191 56.374634 69.660212 62.376573 55.761479 56.535742 67.862806 50.118637 51.272673 54.003958 81.252400 77.330389 65.855771 72.862473 82.694814 59.375192 48.145915 44.711936 80.953705 79.554292 69.155799 67.411408 80.750080 61.330469 54.681888 54.906461 42.654918 35.620734 35.269917
50% 94.208201 80.671016 76.621147 98.699543 99.847372 70.537787 55.657016 52.814589 147.202962 70.834097 74.592091 81.070067 53.928449 55.293798 55.402269 NaN 95.315083 75.994444 108.962743 111.806277 77.476387 57.768955 57.717520 74.620116 67.368666 59.939321 60.310346 72.871556 54.091967 54.724383 57.560303 117.453727 102.822357 81.261827 103.703793 108.451109 76.588783 59.509091 56.736561 90.926518 90.472253 71.671996 73.717128 87.549573 64.386584 57.117897 57.307566 45.203972 40.173297 37.422478
75% 105.650250 87.906132 87.807050 103.679805 104.931149 76.438079 58.533210 58.849791 151.751025 72.567124 78.258475 83.782106 55.470331 57.398486 60.692260 NaN 98.591732 78.148675 112.369067 114.754629 78.879280 59.179083 58.692769 77.112808 70.709486 63.739195 63.406566 78.008657 57.209916 59.418730 65.620191 141.323122 128.564433 94.276219 125.067757 117.235535 87.736983 73.771412 64.923206 120.202757 107.706333 76.492220 82.725102 101.219987 71.138116 62.008205 60.854280 46.234724 44.769346 41.721657
97.5% 117.711876 97.468346 110.509515 108.335215 125.277196 91.561252 62.807131 69.971012 175.099512 193.964647 153.284960 134.583828 70.543404 68.099911 82.103967 NaN 102.214150 87.524096 115.765264 119.509831 80.906720 60.887302 60.655079 86.084977 82.078583 70.693996 72.496147 102.388870 69.023536 68.542420 78.149964 160.141739 151.733667 123.329648 182.228015 154.481630 102.206167 88.307539 72.066345 134.491301 120.000765 87.376019 92.890206 117.639111 89.641035 78.742475 66.709339 49.831682 54.432988 56.307908
max 130.018708 99.718389 120.537862 115.165879 132.967096 110.014158 64.641734 74.620503 326.267108 694.822127 219.394761 197.115729 87.213937 141.959905 128.990140 NaN 108.772527 91.392987 121.951244 120.849700 83.699451 62.485980 62.265714 114.945681 87.924704 82.178646 79.830470 114.276118 70.988370 72.925825 86.511683 160.141739 153.641869 123.838466 204.811975 161.456743 105.433792 98.416609 77.479656 164.998095 142.928318 138.588866 125.289742 187.543788 128.250100 111.255032 80.494622 52.701057 58.409003 60.736481
ValueAverage_hist_optcf_f(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 45.175477 40.237222 36.303819 47.481951 50.994203 34.050025 27.128030 27.708089 79.275421 43.978884 42.241667 46.988858 36.076308 30.178078 30.631365 62.701305 57.080122 43.348422 62.898107 70.691908 46.726974 33.267879 34.606131 43.852098 41.093687 33.099851 35.500079 43.574573 30.953006 30.460430 32.883802 66.534707 62.509990 47.897464 60.056870 64.322450 43.538906 33.636858 34.244757 57.241275 55.410852 40.478193 44.752154 54.405264 40.520470 33.531293 33.451273 30.790701 24.539064 24.305935
std 1.737670 1.901685 3.342362 2.845445 3.864041 3.895063 2.071822 3.342419 5.134065 16.197393 9.472674 6.762032 1.618363 2.503227 4.007041 2.558203 1.662777 1.401016 1.623996 1.745539 1.598961 0.858068 1.091594 4.723929 5.063609 4.158174 4.082144 5.693991 3.200513 3.127798 3.816320 12.151837 12.236261 8.864698 13.502036 9.572891 7.944598 6.599698 6.104059 9.343655 6.604753 3.270237 3.914005 6.981187 4.819349 3.801254 2.141938 1.142863 1.227478 2.260371
min 33.313455 28.391637 29.209682 41.114637 42.012615 25.643363 18.246635 12.993617 60.899811 36.437815 35.801061 40.005112 33.776167 26.829091 21.251504 55.011258 52.674335 40.901957 58.110155 64.704386 43.091132 29.473429 25.390258 33.593195 28.939186 23.871001 25.677652 26.549879 21.980653 21.042636 23.417048 47.526796 44.566424 36.441812 38.790390 39.838320 26.843099 20.533519 21.521889 33.961543 38.811916 30.034810 33.039955 36.890734 24.813719 22.010187 27.745947 27.527130 21.816118 20.040742
2.5% 41.852121 35.982966 31.426159 42.523440 45.338306 28.324880 22.622647 20.772181 72.730362 39.290432 38.860819 44.198561 34.667877 28.212429 26.756687 57.172491 53.600850 41.352215 58.821619 66.606596 43.804245 31.342216 31.511263 34.678524 29.720089 25.084395 26.858685 34.291704 24.681864 23.642875 26.157327 48.546923 45.653807 37.131438 40.261450 47.023664 29.728164 20.964718 22.045894 44.438845 45.299737 36.167194 39.905608 45.778179 34.451172 28.125044 29.741175 29.204024 22.774140 21.489551
25% 44.349947 39.338735 34.138560 45.388035 48.706638 31.444474 25.799738 25.543496 78.034765 40.154849 39.305126 44.922410 35.081047 29.116750 28.318281 60.875639 55.755952 42.607818 62.089156 70.037166 45.943473 32.859338 34.481204 41.402486 38.543199 30.291891 32.139662 39.847108 28.624818 28.051891 30.474410 53.792882 50.284977 40.391129 46.352870 53.856054 36.842006 28.839687 28.851087 48.170289 50.025771 38.252528 41.443186 48.659258 37.921311 32.062366 32.238860 29.941842 23.723351 22.266408
50% 45.325221 40.417082 35.591168 47.221461 50.624526 33.848354 27.562865 27.703784 79.033268 40.427457 39.773405 45.230950 35.470877 29.540111 29.010892 62.599238 56.953720 43.116905 63.002813 70.751555 46.708785 33.328241 34.789771 43.293228 41.132766 33.426375 36.261442 43.106767 31.837814 30.620093 32.132992 66.853404 61.773388 46.395278 60.355995 68.232393 45.406957 33.805654 35.720484 54.546457 54.993100 40.112320 44.514600 52.933508 39.430827 33.158103 33.341268 30.262247 24.467022 24.026835
75% 46.172183 41.439087 37.220480 49.535416 52.423757 35.830691 28.664876 29.722195 80.056644 41.362006 41.296605 46.365705 36.755853 30.359646 31.481967 64.956181 58.375755 43.891171 63.799581 71.642525 47.233286 33.658519 35.030785 46.459535 45.294587 35.552686 38.354987 46.081817 33.204016 32.753133 35.540907 76.797627 75.044306 54.769807 73.577152 69.636073 48.373132 37.519940 39.442471 65.464140 61.176059 42.287993 47.043122 58.898181 42.455278 34.978738 34.818651 31.677827 25.325330 25.506782
97.5% 47.974505 43.070405 44.445137 53.333409 61.901275 42.628439 30.027871 34.382648 90.463622 86.185197 75.644844 67.829329 39.217138 34.887011 42.383883 66.306422 59.685034 49.125540 65.588970 74.614059 49.104235 34.940479 35.830433 51.033141 47.881166 41.380995 42.344131 58.083430 36.204449 35.633473 40.042180 86.088328 80.487189 66.039380 84.777650 81.816996 60.573986 45.744940 45.881320 73.154238 65.827356 46.631942 54.911353 69.185571 52.771575 43.211627 37.085119 32.948971 26.934637 28.577872
max 51.138572 46.800609 54.646675 54.949088 65.550925 51.850500 33.016643 40.043755 180.349037 336.886631 128.588504 137.372822 49.667198 65.213393 52.523915 68.874514 61.840130 49.998567 69.726218 75.338511 56.960907 35.491711 37.047420 70.606498 56.492077 47.209960 46.239649 64.177441 39.863414 39.262600 43.651335 91.530305 83.694936 66.694580 86.942269 84.260185 61.837104 52.523487 47.783136 90.246885 80.060863 71.803614 67.635080 104.543246 72.657013 62.346818 47.560762 35.123601 28.823508 32.996176
ValueAverage_hist_optcf_f(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 47.722470 38.134409 37.446310 43.541327 46.956412 31.951283 25.312546 25.040576 71.740774 38.692348 39.547605 42.837528 29.068410 28.177017 29.195855 NaN 57.386932 43.748047 62.382588 66.191907 43.950614 31.321595 32.941855 42.428204 40.199509 32.955593 35.080771 41.631802 30.607480 30.000587 33.011493 67.730433 62.393870 48.218626 61.475648 62.660579 42.712367 33.351253 32.097764 57.335540 56.011095 41.247943 44.579776 53.977953 38.903443 32.836291 33.341369 22.946866 19.382700 18.983464
std 3.596859 2.811932 5.967216 3.046055 4.445673 5.409374 2.661176 3.514949 4.915115 18.765068 7.240725 5.910189 1.807479 3.125806 4.735743 NaN 1.780798 1.710803 2.028008 1.644101 1.059716 0.896241 1.355520 4.457444 4.644408 3.896611 4.356412 5.886771 3.790689 3.379628 4.318758 13.467296 11.431600 8.555320 15.756086 9.894016 8.115088 7.447802 5.922334 10.109035 7.050657 3.153037 4.659081 6.453600 4.735337 3.784676 2.068701 1.481363 2.207097 3.002755
min 25.656461 28.169699 26.050272 35.050690 36.409243 20.373873 14.917904 13.054663 61.666777 30.100248 34.652613 38.758187 26.106244 24.033717 15.524729 NaN 52.621013 40.226043 56.292143 60.382968 40.433814 28.096233 20.804917 33.139887 28.273194 22.007783 21.237074 21.111324 20.956010 20.228213 21.919904 47.654999 45.205316 36.129485 37.071455 37.196611 21.911577 18.304269 18.583957 28.849814 38.416382 32.385147 33.670349 34.514497 23.846809 21.634801 27.615963 18.415059 13.351143 8.307456
2.5% 42.211340 30.901124 29.585655 37.694997 39.833722 22.282715 19.947675 18.265009 65.244088 33.172320 35.885133 39.976350 27.407564 25.625441 24.536075 NaN 53.724158 41.125231 57.056777 62.512804 41.626862 28.765631 28.618913 34.394593 30.194533 26.177683 25.658877 31.605877 23.158561 22.975925 26.198811 49.003968 46.721866 37.285034 37.793285 45.320932 27.897653 19.123702 19.205457 44.156959 44.901593 36.993894 38.811331 45.200059 33.358857 26.960790 29.321262 18.831548 15.640684 14.315065
25% 45.321445 36.592467 33.463885 41.484507 44.123682 27.438398 24.064748 22.333303 69.764634 34.186375 36.751761 40.787187 28.045733 26.966500 26.483221 NaN 56.252230 42.760607 61.429011 65.337710 43.374614 30.990880 32.667344 39.808056 37.217245 29.778996 32.310780 38.856896 27.918146 27.687678 30.060792 53.986528 51.579024 41.027799 47.789293 53.819205 36.518490 28.125172 27.598300 47.751831 50.078733 39.171773 40.540526 49.336735 35.968213 31.318231 32.263453 22.400026 17.719566 16.302861
50% 46.945908 37.881238 35.546166 43.754006 47.074163 33.193210 25.910069 25.011672 71.076056 34.519891 37.518670 41.357884 28.553451 27.542636 27.621548 NaN 57.200568 43.572244 62.770983 66.180022 44.117595 31.430099 33.125203 41.735509 39.981424 33.230227 35.717012 41.062071 30.718069 29.891158 32.137731 67.335810 62.023122 47.427459 61.095318 66.128568 43.297521 32.537043 33.042092 53.880279 56.037231 40.858529 44.325946 53.510857 37.790873 32.603484 33.385809 23.187285 19.214541 19.410569
75% 50.039086 39.797043 40.595816 46.074434 48.857096 35.233354 27.247834 27.819860 72.951362 35.240706 39.186716 42.802924 29.498469 28.307955 30.482552 NaN 58.563235 44.265220 63.753554 67.254875 44.647653 31.853389 33.549094 44.937974 44.127721 35.264507 37.682468 43.880060 32.767407 32.310833 35.918673 80.122308 74.320041 53.893217 71.180449 68.526742 49.640432 39.780411 36.559460 66.113581 62.014214 42.830309 47.240138 57.769963 41.249711 34.501592 34.691266 24.051309 20.592927 20.734315
97.5% 55.642947 44.220885 50.444815 48.792808 58.346928 41.658164 28.627464 31.960051 79.821264 86.487046 67.099756 59.544351 34.354405 33.870302 42.669838 NaN 61.230793 50.535786 65.283695 69.252369 45.833692 32.691725 34.498770 49.908430 48.043689 40.205422 44.314321 57.770145 38.491947 36.741395 42.263403 88.484402 86.594131 68.382460 103.261302 87.979731 57.082776 47.721725 41.424692 75.754769 67.785406 47.885189 55.353135 66.885117 51.498965 42.391175 36.746131 24.892340 23.537740 25.795319
max 56.869385 44.866865 54.572420 51.113866 61.328208 50.599382 33.178101 35.075066 138.007656 300.006977 95.318513 89.325593 48.368216 72.213658 63.937091 NaN 61.772892 51.676310 68.044938 70.148506 48.159494 33.983111 35.346495 71.667337 55.342015 51.878695 46.308729 63.999868 40.116250 39.221507 44.407004 89.056471 87.563832 68.664584 114.125099 91.952168 63.448290 56.407343 43.032461 85.487338 74.915530 72.548234 68.940266 101.926090 70.404004 57.564935 47.245435 25.880763 26.436307 28.116618
ValueFactor_hist_optcf_f(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.110444 1.193349 1.148942 1.064677 1.021081 0.977549 0.903693 0.801583 1.597733 1.403138 1.203839 1.151099 1.350195 1.322542 1.249446 1.138182 1.126206 1.118057 1.075804 1.071918 1.090439 1.100763 1.050301 1.220296 1.220006 1.186538 1.146395 1.110429 1.155971 1.179214 1.177995 1.142858 1.152084 1.164686 1.130840 1.068750 1.150780 1.181556 1.120997 1.176081 1.214755 1.168228 1.157981 1.088965 1.166595 1.171990 1.140107 0.972401 0.873984 0.765088
std 0.014350 0.015394 0.041868 0.022447 0.021162 0.050178 0.044231 0.062013 0.037601 0.084605 0.085153 0.055240 0.024463 0.042867 0.054614 0.007917 0.010699 0.011466 0.010408 0.023345 0.022383 0.007103 0.012271 0.025432 0.033600 0.042726 0.034594 0.046720 0.035481 0.029733 0.036575 0.024720 0.035165 0.048053 0.028136 0.034749 0.032840 0.077645 0.044109 0.040093 0.035213 0.018591 0.019657 0.026325 0.028020 0.022072 0.032445 0.035397 0.039732 0.063360
min 1.056745 1.097519 1.035543 1.003922 0.923115 0.806810 0.700139 0.451965 1.323257 1.163643 1.110578 0.948250 1.222508 1.241004 0.860535 1.119896 1.082202 1.091326 1.031159 0.966255 0.984190 1.079167 1.007036 1.171007 1.154172 1.093460 1.047737 0.973476 0.981508 1.120423 1.111534 1.099664 1.103470 1.101220 1.065151 0.884865 1.016456 1.074447 1.041994 0.989044 1.092808 1.122762 1.106164 0.961911 1.062234 1.117059 0.965958 0.883659 0.794868 0.647222
2.5% 1.083454 1.163646 1.095132 1.031357 0.980434 0.879919 0.793333 0.671348 1.531973 1.345093 1.154867 1.118458 1.319398 1.286726 1.192033 1.122241 1.103681 1.105307 1.051325 1.025123 1.054573 1.088616 1.030922 1.177151 1.170519 1.120714 1.103441 1.053315 1.107577 1.133737 1.129260 1.104611 1.105804 1.108591 1.089349 0.986165 1.089643 1.089912 1.061121 1.120089 1.154070 1.140766 1.128262 1.044027 1.115953 1.135154 1.092015 0.922238 0.816979 0.693864
25% 1.100363 1.184714 1.121083 1.049349 1.004867 0.935731 0.871896 0.761790 1.576816 1.365590 1.169742 1.132721 1.334518 1.302276 1.212436 1.132650 1.118994 1.110817 1.072540 1.061366 1.077824 1.097062 1.043044 1.200330 1.194969 1.160643 1.125080 1.079152 1.134398 1.155939 1.151939 1.119568 1.118197 1.142229 1.107144 1.059591 1.134572 1.143814 1.101077 1.151975 1.193487 1.155760 1.144717 1.072390 1.143606 1.157676 1.119698 0.945730 0.849933 0.714267
50% 1.109605 1.193562 1.139462 1.059504 1.022801 0.993296 0.919551 0.816433 1.591313 1.376841 1.182064 1.137982 1.343341 1.311012 1.232272 1.138015 1.128503 1.115548 1.077531 1.069726 1.087579 1.099718 1.048331 1.219608 1.214730 1.175683 1.137146 1.101950 1.146485 1.173928 1.173494 1.152851 1.150326 1.150527 1.133439 1.082699 1.152415 1.157107 1.118062 1.165117 1.207836 1.165354 1.153043 1.086669 1.164359 1.169964 1.137926 0.955955 0.872516 0.754621
75% 1.121073 1.201464 1.159033 1.072330 1.033891 1.007882 0.938259 0.843112 1.615690 1.401108 1.201482 1.151009 1.365563 1.328559 1.271679 1.144296 1.134562 1.123743 1.081940 1.081777 1.102349 1.103239 1.055324 1.235362 1.235564 1.205976 1.157770 1.133950 1.174128 1.200828 1.191547 1.164764 1.183222 1.157951 1.142880 1.086780 1.162718 1.200988 1.132290 1.187071 1.231634 1.176903 1.167761 1.104086 1.192991 1.187087 1.153789 1.007043 0.888963 0.790495
97.5% 1.134649 1.232153 1.255920 1.118123 1.062420 1.061294 0.957645 0.915864 1.684966 1.709042 1.532025 1.294104 1.396932 1.429653 1.389966 1.152741 1.141510 1.163878 1.091152 1.123353 1.131609 1.118150 1.076808 1.274763 1.293309 1.282107 1.221074 1.216974 1.236026 1.237403 1.268782 1.185650 1.195983 1.262294 1.186220 1.109411 1.248784 1.443335 1.256380 1.249406 1.289521 1.212022 1.197563 1.143066 1.217042 1.214015 1.202961 1.035620 0.961668 0.899100
max 1.139584 1.253313 1.367523 1.131657 1.075579 1.085500 1.000511 0.976529 1.779045 1.940491 1.813841 1.664182 1.534029 1.727105 1.507225 1.153784 1.147259 1.173878 1.130427 1.153818 1.202821 1.131670 1.207329 1.304819 1.301418 1.326116 1.253033 1.249215 1.244111 1.266791 1.310353 1.198685 1.263254 1.341058 1.191331 1.131118 1.305214 1.626424 1.405340 1.357076 1.368157 1.272452 1.282994 1.167482 1.302858 1.300367 1.286612 1.045935 0.980711 0.940674
ValueFactor_hist_optcf_f(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.076385 1.128881 1.163639 1.035682 0.979687 0.935351 0.843889 0.788523 1.540910 1.331508 1.183320 1.098544 1.203592 1.291599 1.211012 NaN 1.129131 1.131662 1.074999 1.031408 1.053954 1.072517 0.996258 1.230620 1.223286 1.201184 1.141932 1.078776 1.161812 1.181211 1.196760 1.149758 1.176439 1.191559 1.164109 1.091420 1.138383 1.170128 1.069274 1.178286 1.229746 1.181455 1.174780 1.106187 1.140445 1.170188 1.142499 1.071532 0.854701 0.765804
std 0.039554 0.037368 0.078174 0.033217 0.041908 0.097212 0.064592 0.079220 0.048391 0.121905 0.085964 0.045604 0.035983 0.059359 0.080243 NaN 0.010387 0.016744 0.012499 0.028346 0.021660 0.014107 0.031355 0.041639 0.049303 0.050387 0.035957 0.049684 0.046957 0.042347 0.050140 0.033788 0.029593 0.038740 0.027715 0.043266 0.059533 0.091498 0.059612 0.042762 0.030648 0.026855 0.040830 0.034515 0.032433 0.030664 0.047447 0.027538 0.077395 0.149520
min 0.883010 1.012611 0.965454 0.932919 0.827715 0.677990 0.576613 0.469798 1.362208 1.193384 1.042890 0.963705 1.131437 1.191316 0.541563 NaN 1.100954 1.084208 1.028125 0.919583 0.956089 1.047963 0.941391 1.150118 1.137031 1.099574 1.028934 0.976499 0.978954 1.106253 1.034639 1.076050 1.129086 1.126580 1.082293 0.869195 0.951044 1.056039 0.983216 0.872169 1.090315 1.131937 1.084954 0.980578 1.026705 1.071764 0.873785 0.933781 0.696110 0.545964
2.5% 1.028627 1.063988 1.050626 0.950516 0.885077 0.728756 0.703206 0.639226 1.468502 1.250695 1.113680 1.058282 1.165971 1.243550 1.137975 NaN 1.114080 1.112532 1.047879 0.983499 1.011337 1.052224 0.970608 1.172068 1.158796 1.126294 1.090868 1.007407 1.096754 1.121498 1.123872 1.089418 1.132348 1.139359 1.110559 0.991331 1.037231 1.064117 1.004091 1.108823 1.162935 1.143651 1.130053 1.042859 1.076479 1.120523 1.058271 1.012965 0.742472 0.579391
25% 1.047898 1.107970 1.110163 1.012383 0.950848 0.850840 0.798293 0.728410 1.506037 1.275370 1.143319 1.080248 1.182944 1.265507 1.160394 NaN 1.120877 1.121657 1.065544 1.014415 1.037846 1.064172 0.981646 1.194158 1.190454 1.169766 1.120056 1.038193 1.132202 1.148093 1.162683 1.122972 1.158284 1.168706 1.151521 1.069719 1.113955 1.125329 1.035448 1.143946 1.211526 1.162376 1.151843 1.079436 1.117918 1.145908 1.116828 1.058510 0.809748 0.625515
50% 1.062646 1.120612 1.145248 1.041000 0.992912 0.976788 0.870209 0.811701 1.533552 1.290854 1.160513 1.087337 1.196270 1.276788 1.184371 NaN 1.126617 1.128399 1.077664 1.027523 1.054393 1.071098 0.988141 1.222089 1.212205 1.190303 1.135742 1.069602 1.156216 1.177482 1.191962 1.144497 1.177433 1.184282 1.166363 1.110393 1.129825 1.164329 1.051551 1.177101 1.224964 1.177129 1.164494 1.110727 1.144031 1.165288 1.144193 1.069051 0.850056 0.741065
75% 1.097451 1.151650 1.186980 1.059612 1.006352 1.009649 0.890129 0.851000 1.567627 1.325862 1.188607 1.101589 1.212809 1.294037 1.241396 NaN 1.136410 1.137501 1.085202 1.046358 1.069807 1.076375 0.998443 1.267007 1.243710 1.221510 1.156235 1.112969 1.185323 1.204782 1.227363 1.183361 1.200878 1.208718 1.175521 1.119498 1.151370 1.184590 1.081391 1.206069 1.254168 1.190039 1.183776 1.135929 1.161939 1.197736 1.165733 1.087674 0.890366 0.845970
97.5% 1.157595 1.229773 1.347417 1.083893 1.059011 1.072257 0.927579 0.905468 1.641696 1.760667 1.501106 1.231143 1.310437 1.410076 1.447983 NaN 1.150811 1.200920 1.092205 1.097526 1.093585 1.103004 1.090158 1.314256 1.326719 1.318667 1.221843 1.195860 1.277927 1.276642 1.317364 1.201752 1.223421 1.272329 1.239269 1.139793 1.335012 1.503750 1.261377 1.253093 1.288304 1.239452 1.326460 1.158043 1.198401 1.224913 1.228505 1.125369 1.007720 1.033176
max 1.179287 1.237704 1.404304 1.122203 1.079433 1.114281 0.971997 0.937086 1.792053 2.121927 1.701891 1.532755 1.528232 1.796781 1.620960 NaN 1.155443 1.213145 1.096285 1.128290 1.109420 1.214950 1.482201 1.370531 1.381830 1.361444 1.282900 1.252162 1.328237 1.365266 1.371905 1.210511 1.289410 1.310590 1.274053 1.173316 1.516640 1.703000 1.447970 1.333850 1.360069 1.285923 1.364994 1.314645 1.457411 1.290563 1.287909 1.142447 1.045332 1.073048

Curtailment (fixed-tilt)

In [25]:
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CF_curtail/CF_mustrun(fixed)(da)',
    'CF_curtail/CF_mustrun(fixed)(rt)',
    'Rev_curtail/Rev_mustrun(fixed)(da)',
    'Rev_curtail/Rev_mustrun(fixed)(rt)',
]
colindex = [0, 0, 1, 1]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 2
ylim = [
    [0.68, 1.02],
    [0.98, 1.32],
]

ylabel = [
    'Capacity Factor',
    'Revenue',
]

note = [
    '',
    '',
]
y1 = 1     # 1.2 if using note
y2 = 1.04  # 1.07 if using note

gridspec_kw = {'width_ratios': [2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
#             format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.1))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Curtailable vs. Must-run, fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
In [26]:
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
CF_curtail/CF_mustrun(fixed)(da) CF_curtail/CF_mustrun(fixed)(rt) Rev_curtail/Rev_mustrun(fixed)(da) Rev_curtail/Rev_mustrun(fixed)(rt)
count 2209.000000 2209.000000 2209.000000 2209.000000
mean 0.946114 0.839164 1.012793 1.116364
std 0.025391 0.028136 0.025054 0.088171
min 0.734967 0.704510 1.003795 1.058217
2.5% 0.873700 0.755800 1.005002 1.062509
25% 0.945421 0.829134 1.006094 1.071480
50% 0.954375 0.844459 1.007082 1.090087
75% 0.959726 0.860207 1.008928 1.123006
97.5% 0.963765 0.867057 1.054005 1.305881
max 0.968056 0.879552 1.486920 2.011486
median
CF_curtail/CF_mustrun(fixed)(da) CF_curtail/CF_mustrun(fixed)(rt) Rev_curtail/Rev_mustrun(fixed)(da) Rev_curtail/Rev_mustrun(fixed)(rt)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 0.999891 NaN 1.000000 1.0 1.0 1.0 NaN 0.992574 NaN NaN 0.994818 0.998674 0.999160 NaN 1.000005 NaN 1.000000 1.0 1.0 1.0 NaN 1.003201 NaN NaN 1.002599 1.002117 1.000501 NaN
2011 0.999336 1.0 1.000000 1.0 1.0 1.0 NaN 0.954748 0.999052 0.999084 0.994701 0.999010 0.999012 NaN 1.000028 1.0 1.000000 1.0 1.0 1.0 NaN 1.008916 1.000475 1.000000 1.003016 1.000875 1.000101 NaN
2012 1.000000 1.0 1.000000 1.0 1.0 1.0 NaN 0.993981 0.999194 0.999549 0.996336 0.999521 0.999780 NaN 1.000000 1.0 1.000000 1.0 1.0 1.0 NaN 1.007790 1.000890 1.000000 1.002905 1.000702 1.000071 NaN
2013 1.000000 1.0 1.000000 1.0 1.0 1.0 NaN 0.988362 0.999825 0.999294 0.996476 0.999359 1.000000 NaN 1.000000 1.0 1.000000 1.0 1.0 1.0 NaN 1.011310 1.000137 1.000000 1.002096 1.000941 1.000000 NaN
2014 0.996845 1.0 1.000000 1.0 1.0 1.0 NaN 0.968861 0.999016 0.989827 0.997497 0.999057 0.998801 NaN 1.000016 1.0 1.000000 1.0 1.0 1.0 NaN 1.016974 1.000153 1.000098 1.001488 1.000601 1.000516 NaN
2015 0.999576 1.0 0.999983 1.0 1.0 1.0 1.000000 0.950535 0.997184 0.997776 0.997206 0.998593 0.998381 0.956260 1.000039 1.0 1.000002 1.0 1.0 1.0 1.000000 1.026694 1.000627 1.001910 1.001491 1.001117 1.001004 1.062874
2016 0.994956 1.0 1.000000 1.0 1.0 1.0 0.999761 0.892457 0.998501 0.991553 0.997408 0.997897 0.999278 0.900596 1.000723 1.0 1.000000 1.0 1.0 1.0 1.000001 1.063702 1.000980 1.010817 1.002235 1.004416 1.000335 1.088473
2017 0.954375 1.0 1.000000 1.0 1.0 1.0 0.944457 0.844459 0.997431 0.995044 0.997846 0.998177 0.999005 0.846251 1.007082 1.0 1.000000 1.0 1.0 1.0 1.007793 1.090087 1.001346 1.003300 1.001176 1.001133 1.000261 1.161483
max
CF_curtail/CF_mustrun(fixed)(da) CF_curtail/CF_mustrun(fixed)(rt) Rev_curtail/Rev_mustrun(fixed)(da) Rev_curtail/Rev_mustrun(fixed)(rt)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 0.999967 NaN 1.000000 1.0 1.0 1.0 NaN 0.995393 NaN NaN 0.999929 0.999217 0.999989 NaN 1.007511 NaN 1.000381 1.032036 1.000083 1.088616 NaN 1.283891 NaN NaN 1.089373 1.032796 1.459818 NaN
2011 0.999751 1.0 1.000000 1.0 1.0 1.0 NaN 0.962186 0.999963 0.999673 0.999722 0.999722 0.999872 NaN 1.017282 1.181707 1.000089 1.006524 1.000000 1.056131 NaN 1.129260 1.115124 1.016475 1.065589 1.014703 1.112005 NaN
2012 1.000000 1.0 1.000000 1.0 1.0 1.0 NaN 0.996781 0.999995 0.999770 1.000000 0.999899 0.999985 NaN 1.062973 1.441546 1.001437 1.104133 1.000915 1.040329 NaN 1.244553 1.109393 1.016019 1.369107 1.028196 1.107201 NaN
2013 1.000000 1.0 1.000000 1.0 1.0 1.0 NaN 0.992584 1.000000 0.999662 1.000000 0.999894 1.000000 NaN 1.011328 1.020913 1.003430 1.105501 1.003456 1.031728 NaN 1.075805 1.094200 1.001646 1.465850 1.061580 1.068820 NaN
2014 1.000000 1.0 1.000000 1.0 1.0 1.0 NaN 0.975334 0.999933 0.994899 1.000000 0.999388 0.999968 NaN 1.019664 1.063308 1.000731 1.065033 1.000000 1.029761 NaN 1.155153 1.055808 1.003568 1.419973 1.053398 1.150497 NaN
2015 1.000000 1.0 0.999989 1.0 1.0 1.0 1.000000 0.958292 0.999841 0.998326 1.000000 0.999299 0.999973 0.966608 1.038759 1.024715 1.006559 1.072204 1.002804 1.139016 1.000179 1.304408 1.065644 1.004631 1.331792 1.084671 1.264873 1.144309
2016 1.000000 1.0 1.000000 1.0 1.0 1.0 1.000000 0.912507 0.999521 0.993714 1.000000 0.999218 0.999996 0.922624 1.059724 1.061083 1.021643 1.023589 1.001834 1.033304 1.009883 1.396916 1.111239 1.064501 1.119620 1.260819 1.154599 1.333018
2017 0.968056 1.0 1.000000 1.0 1.0 1.0 0.964118 0.879552 0.999973 0.996733 1.000000 0.999243 0.999991 0.917047 1.486920 1.072424 1.190638 1.036037 1.001351 1.015338 1.033989 2.011486 1.344622 1.461626 1.244391 1.213275 1.091430 2.012393
CF_curtail/CF_mustrun(fixed)(da)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.0 424.000000 430.000000 434.0 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.999801 0.998804 0.999370 0.999726 0.996999 0.997345 0.992236 0.946114 0.997992 0.999090 0.999804 0.999692 0.999823 0.999529 0.998931 0.999974 0.999990 0.999948 0.999975 0.999959 0.999947 0.999912 0.999363 0.999387 0.999531 0.997503 0.997525 0.998416 0.998493 0.999312 0.999245 0.999993 1.0 0.999987 0.999778 1.0 0.999943 0.999908 0.999966 0.999617 0.999901 0.999702 0.999877 0.999837 0.999740 0.999781 0.999849 0.999988 0.998109 0.940377
std 0.000511 0.003582 0.003594 0.001185 0.001484 0.003702 0.009445 0.025391 0.006467 0.007907 0.001499 0.002042 0.001033 0.002064 0.004179 0.000198 0.000142 0.000498 0.000146 0.000265 0.000355 0.000757 0.005176 0.003683 0.001620 0.011433 0.010022 0.007519 0.005545 0.003129 0.003622 0.000081 0.0 0.000154 0.001583 0.0 0.000504 0.000574 0.000343 0.003466 0.001412 0.002565 0.001759 0.001062 0.002272 0.001973 0.001152 0.000093 0.003126 0.013541
min 0.994518 0.926922 0.947499 0.988563 0.980110 0.954078 0.920260 0.734967 0.854365 0.725999 0.970030 0.958049 0.985618 0.961378 0.920014 0.997526 0.997901 0.991583 0.998082 0.997303 0.992127 0.983212 0.893201 0.954895 0.985832 0.873062 0.903590 0.876523 0.950844 0.954013 0.945890 0.999055 1.0 0.998168 0.982580 1.0 0.992872 0.994425 0.995167 0.915006 0.952195 0.929750 0.954900 0.975433 0.937845 0.958886 0.978373 0.999101 0.987920 0.887235
2.5% 0.998756 0.994915 0.994438 0.994905 0.995411 0.990164 0.972038 0.873700 0.982063 0.992659 0.998448 0.997504 0.997905 0.994869 0.991996 1.000000 1.000000 0.999519 0.999372 0.999439 0.999774 0.999222 0.997508 0.993915 0.994171 0.982243 0.985092 0.990624 0.984293 0.992985 0.994990 1.000000 1.0 1.000000 0.996282 1.0 0.999994 0.998539 1.000000 0.997823 0.999525 0.997965 0.999936 0.997812 0.998171 0.998342 0.998936 1.000000 0.991894 0.924118
25% 0.999857 0.999225 1.000000 1.000000 0.996732 0.995141 0.987382 0.945421 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.999961 1.000000 1.000000 1.000000 1.000000 1.000000 0.999979 1.000000 1.000000 1.000000 0.999990 0.999189 0.999401 0.999653 0.999876 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.998871 0.927142
50% 0.999891 0.999336 1.000000 1.000000 0.996845 0.999576 0.994956 0.954375 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.999983 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.999761 0.944457
75% 0.999918 0.999552 1.000000 1.000000 0.997110 1.000000 1.000000 0.959726 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.999985 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.950900
97.5% 0.999956 0.999676 1.000000 1.000000 0.999497 1.000000 1.000000 0.963765 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.999987 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.958160
max 0.999967 0.999751 1.000000 1.000000 1.000000 1.000000 1.000000 0.968056 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.999989 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.964118
CF_curtail/CF_mustrun(fixed)(rt)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.992355 0.952957 0.992018 0.987062 0.963292 0.920131 0.875298 0.839164 0.986608 0.992268 0.997905 0.998359 0.996696 0.994842 0.990580 NaN 0.999054 0.999460 0.999085 0.990215 0.997744 0.991003 0.991827 0.991375 0.989409 0.990109 0.988523 0.993012 0.993079 0.994000 0.995170 0.997564 0.998627 0.998951 0.996754 0.997361 0.994824 0.989016 0.991905 0.996896 0.997467 0.998516 0.999284 0.997900 0.996624 0.997759 0.998232 0.955709 0.890160 0.845761
std 0.009986 0.007156 0.004195 0.006190 0.009657 0.040977 0.031010 0.028136 0.033568 0.019451 0.004836 0.003140 0.002676 0.007921 0.017115 NaN 0.000437 0.000526 0.001466 0.001185 0.000413 0.003110 0.011210 0.010055 0.013950 0.017380 0.021710 0.015424 0.011220 0.008866 0.007534 0.001939 0.001604 0.001615 0.010943 0.005872 0.009497 0.017054 0.014515 0.005607 0.003634 0.005336 0.002582 0.003297 0.006902 0.005329 0.002853 0.005161 0.021461 0.034932
min 0.762575 0.910830 0.961229 0.953063 0.917312 0.825962 0.741424 0.704510 0.845957 0.803607 0.939958 0.954757 0.977393 0.950507 0.708194 NaN 0.996023 0.989842 0.985119 0.987323 0.994792 0.963586 0.902317 0.954254 0.918662 0.837002 0.821874 0.799943 0.912106 0.924050 0.941941 0.991223 0.988063 0.988991 0.927052 0.958637 0.948170 0.927682 0.928524 0.914363 0.961644 0.925965 0.954423 0.940864 0.907735 0.933168 0.968333 0.931374 0.846265 0.729627
2.5% 0.989753 0.929307 0.982634 0.969009 0.940646 0.844756 0.818729 0.755800 0.879823 0.930233 0.984200 0.993137 0.991756 0.972728 0.950395 NaN 0.997809 0.998420 0.997898 0.988325 0.996644 0.982210 0.957609 0.963912 0.948932 0.950177 0.918648 0.964714 0.958203 0.970717 0.971678 0.991645 0.993705 0.992185 0.939498 0.969798 0.952034 0.935243 0.933548 0.976356 0.987003 0.989693 0.994945 0.990003 0.982694 0.988690 0.988392 0.943406 0.851520 0.790500
25% 0.991982 0.951287 0.988919 0.985803 0.957079 0.875198 0.842222 0.829134 0.997920 0.996519 0.998651 0.998540 0.995315 0.995820 0.992877 NaN 0.998945 0.999481 0.999228 0.989619 0.997648 0.991201 0.994317 0.987855 0.988634 0.990898 0.989331 0.993995 0.992088 0.994065 0.994075 0.996628 0.998454 0.999080 0.998141 0.997826 0.995242 0.988822 0.990889 0.995880 0.996701 0.999083 0.999494 0.997275 0.995852 0.997468 0.997891 0.952065 0.878129 0.813131
50% 0.992574 0.954748 0.993981 0.988362 0.968861 0.950535 0.892457 0.844459 0.999052 0.999194 0.999825 0.999016 0.997184 0.998501 0.997431 NaN 0.999084 0.999549 0.999294 0.989827 0.997776 0.991553 0.995044 0.994818 0.994701 0.996336 0.996476 0.997497 0.997206 0.997408 0.997846 0.998674 0.999010 0.999521 0.999359 0.999057 0.998593 0.997897 0.998177 0.999160 0.999012 0.999780 1.000000 0.998801 0.998381 0.999278 0.999005 0.956260 0.900596 0.846251
75% 0.993928 0.957670 0.995049 0.990578 0.970922 0.954159 0.902456 0.860207 0.999652 0.999869 0.999951 0.999287 0.998847 0.998974 0.998942 NaN 0.999331 0.999646 0.999466 0.990368 0.997941 0.992251 0.995230 0.998810 0.997331 0.998968 0.999407 0.998834 0.999239 0.999363 0.999600 0.998975 0.999673 0.999683 0.999787 0.999309 0.999064 0.998769 0.999055 0.999793 0.999264 0.999880 1.000000 0.999607 0.999741 0.999903 0.999891 0.960038 0.905443 0.874680
97.5% 0.995014 0.961060 0.996112 0.992126 0.972243 0.957265 0.908433 0.867057 0.999934 0.999988 0.999995 0.999591 0.999752 0.999384 0.999884 NaN 0.999470 0.999743 0.999587 0.992944 0.998188 0.993237 0.996142 0.999892 0.999578 0.999778 0.999979 0.999980 1.000000 1.000000 1.000000 0.999212 0.999722 0.999839 0.999854 0.999388 0.999143 0.999059 0.999169 0.999930 0.999733 0.999955 1.000000 0.999957 0.999922 0.999972 0.999971 0.963017 0.912190 0.900861
max 0.995393 0.962186 0.996781 0.992584 0.975334 0.958292 0.912507 0.879552 0.999963 0.999995 1.000000 0.999933 0.999841 0.999521 0.999973 NaN 0.999673 0.999770 0.999662 0.994899 0.998326 0.993714 0.996733 0.999929 0.999722 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.999217 0.999722 0.999899 0.999894 0.999388 0.999299 0.999218 0.999243 0.999989 0.999872 0.999985 1.000000 0.999968 0.999973 0.999996 0.999991 0.966608 0.922624 0.917047
Rev_curtail/Rev_mustrun(fixed)(da)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.0 424.000000 430.000000 434.0 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.000073 1.000480 1.000599 1.000094 1.000156 1.000790 1.001747 1.012793 1.000648 1.000522 1.000100 1.000367 1.000110 1.000582 1.000623 1.000006 1.000000 1.000009 1.000011 1.000005 1.000024 1.000029 1.000275 1.000299 1.000099 1.001640 1.001969 1.000838 1.000996 1.000335 1.000535 1.000001 1.0 1.000006 1.000068 1.0 1.000017 1.000013 1.000008 1.000222 1.000067 1.000130 1.000070 1.000091 1.000172 1.000098 1.000072 1.000002 1.000273 1.009808
std 0.000463 0.001704 0.003897 0.000569 0.001055 0.003141 0.004442 0.025054 0.007241 0.011310 0.000834 0.003050 0.000985 0.003703 0.003378 0.000045 0.000004 0.000079 0.000151 0.000054 0.000324 0.000752 0.006161 0.002449 0.000530 0.010325 0.011180 0.004104 0.006070 0.001961 0.002913 0.000007 0.0 0.000077 0.000414 0.0 0.000181 0.000142 0.000095 0.003163 0.001505 0.001492 0.000956 0.000794 0.002586 0.000939 0.000689 0.000014 0.000628 0.004427
min 1.000000 1.000009 1.000000 1.000000 1.000000 1.000000 1.000000 1.003795 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000001 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.004525
2.5% 1.000002 1.000013 1.000000 1.000000 1.000000 1.000000 1.000000 1.005002 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000001 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.005443
25% 1.000004 1.000019 1.000000 1.000000 1.000000 1.000000 1.000000 1.006094 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000002 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.006652
50% 1.000005 1.000028 1.000000 1.000000 1.000016 1.000039 1.000723 1.007082 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000002 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000001 1.007793
75% 1.000007 1.000044 1.000000 1.000000 1.000021 1.000625 1.001461 1.008928 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000001 1.000000 1.000000 1.000000 1.000000 1.000000 1.000002 1.000000 1.000000 1.000000 1.000000 1.000039 1.000074 1.000067 1.000008 1.000000 1.000000 1.000000 1.0 1.000000 1.000000 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000020 1.013532
97.5% 1.000782 1.007509 1.003848 1.000906 1.000922 1.004448 1.012152 1.054005 1.002237 1.001611 1.001175 1.003106 1.000730 1.006612 1.007219 1.000000 1.000000 1.000028 1.000005 1.000005 1.000004 1.000000 1.000315 1.002445 1.001253 1.005218 1.011240 1.009122 1.006600 1.002129 1.006186 1.000000 1.0 1.000000 1.001313 1.0 1.000000 1.000005 1.000000 1.000520 1.000070 1.000538 1.000011 1.000733 1.000581 1.000489 1.000354 1.000000 1.001060 1.014892
max 1.007511 1.017282 1.062973 1.011328 1.019664 1.038759 1.059724 1.486920 1.181707 1.441546 1.020913 1.063308 1.024715 1.061083 1.072424 1.000381 1.000089 1.001437 1.003430 1.000731 1.006559 1.021643 1.190638 1.032036 1.006524 1.104133 1.105501 1.065033 1.072204 1.023589 1.036037 1.000083 1.0 1.000915 1.003456 1.0 1.002804 1.001834 1.001351 1.088616 1.056131 1.040329 1.031728 1.029761 1.139016 1.033304 1.015338 1.000179 1.009883 1.033989
Rev_curtail/Rev_mustrun(fixed)(rt)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.004650 1.014679 1.018781 1.014150 1.027885 1.081936 1.081495 1.116364 1.004388 1.003656 1.001417 1.001397 1.002758 1.003635 1.007032 NaN 1.000203 1.000122 1.000041 1.000191 1.001919 1.012073 1.007956 1.007112 1.006992 1.012386 1.014515 1.008891 1.008607 1.007034 1.006288 1.004739 1.001682 1.001970 1.003391 1.002043 1.003628 1.018964 1.013387 1.002564 1.001150 1.001030 1.000635 1.001896 1.003505 1.002271 1.002405 1.066430 1.105749 1.212859
std 0.012589 0.017010 0.027827 0.010857 0.016902 0.074476 0.038637 0.088171 0.009921 0.008257 0.004874 0.004587 0.006477 0.007230 0.017579 NaN 0.001448 0.000818 0.000140 0.000309 0.000328 0.005884 0.022555 0.011872 0.010813 0.034750 0.052513 0.031862 0.029981 0.014490 0.019655 0.004616 0.002118 0.004431 0.009714 0.005144 0.009011 0.047474 0.039700 0.011388 0.004385 0.005312 0.003330 0.006482 0.010221 0.008473 0.004666 0.013734 0.042090 0.150963
min 1.001490 1.005180 1.003763 1.004397 1.012153 1.012642 1.040916 1.058217 1.000000 1.000000 1.000000 1.000000 1.000000 1.000004 1.000001 NaN 1.000000 1.000000 1.000000 1.000013 1.001295 1.006703 1.002079 1.000044 1.000041 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000983 1.000267 1.000113 1.000023 1.000261 1.000370 1.000398 1.000282 1.000001 1.000006 1.000005 1.000000 1.000000 1.000030 1.000001 1.000000 1.045542 1.052763 1.034115
2.5% 1.001740 1.005644 1.005006 1.004943 1.013085 1.015725 1.045254 1.062509 1.000006 1.000015 1.000000 1.000003 1.000005 1.000028 1.000046 NaN 1.000000 1.000000 1.000000 1.000026 1.001470 1.007252 1.002489 1.000061 1.000053 1.000048 1.000001 1.000004 1.000000 1.000000 1.000000 1.000983 1.000267 1.000197 1.000032 1.000261 1.000388 1.000398 1.000282 1.000003 1.000017 1.000015 1.000000 1.000000 1.000143 1.000010 1.000004 1.049361 1.058312 1.051075
25% 1.002439 1.007019 1.006209 1.007427 1.015084 1.021240 1.051116 1.071480 1.000102 1.000263 1.000046 1.000058 1.000160 1.000283 1.000655 NaN 1.000000 1.000000 1.000000 1.000060 1.001764 1.010001 1.003210 1.000318 1.001469 1.000769 1.000114 1.000583 1.000169 1.000353 1.000108 1.001290 1.000335 1.000280 1.000218 1.000299 1.000500 1.000525 1.000624 1.000014 1.000045 1.000034 1.000000 1.000022 1.000307 1.000045 1.000010 1.054289 1.072477 1.083456
50% 1.003201 1.008916 1.007790 1.011310 1.016974 1.026694 1.063702 1.090087 1.000475 1.000890 1.000137 1.000153 1.000627 1.000980 1.001346 NaN 1.000000 1.000000 1.000000 1.000098 1.001910 1.010817 1.003300 1.002599 1.003016 1.002905 1.002096 1.001488 1.001491 1.002235 1.001176 1.002117 1.000875 1.000702 1.000941 1.000601 1.001117 1.004416 1.001133 1.000501 1.000101 1.000071 1.000000 1.000516 1.001004 1.000335 1.000261 1.062874 1.088473 1.161483
75% 1.003556 1.015572 1.016607 1.019119 1.040257 1.153218 1.105764 1.123006 1.002596 1.002714 1.000885 1.000573 1.002280 1.004027 1.006375 NaN 1.000000 1.000000 1.000000 1.000150 1.002042 1.011512 1.003782 1.009851 1.007876 1.011915 1.006609 1.004169 1.005576 1.006883 1.004176 1.007782 1.001933 1.001544 1.002522 1.001371 1.002055 1.014347 1.006032 1.002333 1.001184 1.000233 1.000114 1.001325 1.002432 1.001312 1.004162 1.077649 1.127282 1.369467
97.5% 1.017662 1.071168 1.107211 1.042849 1.066153 1.239133 1.168655 1.305881 1.028339 1.026781 1.012096 1.012382 1.020464 1.020922 1.040413 NaN 1.000812 1.000862 1.000513 1.000927 1.002466 1.028604 1.065609 1.031893 1.043540 1.077718 1.084180 1.084611 1.047479 1.054960 1.034565 1.014443 1.007800 1.022849 1.052338 1.023656 1.040612 1.224227 1.195182 1.017574 1.009860 1.011176 1.005327 1.012424 1.022062 1.016620 1.014445 1.094313 1.203843 1.473657
max 1.283891 1.129260 1.244553 1.075805 1.155153 1.304408 1.396916 2.011486 1.115124 1.109393 1.094200 1.055808 1.065644 1.111239 1.344622 NaN 1.016475 1.016019 1.001646 1.003568 1.004631 1.064501 1.461626 1.089373 1.065589 1.369107 1.465850 1.419973 1.331792 1.119620 1.244391 1.032796 1.014703 1.028196 1.061580 1.053398 1.084671 1.260819 1.213275 1.459818 1.112005 1.107201 1.068820 1.150497 1.264873 1.154599 1.091430 1.144309 1.333018 2.012393

Tracking

In [28]:
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun',
    'CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun',
    'CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
    'Revenue_track(def)/fixed(optcf)_hist,da,mustrun',
    'Revenue_track(def)/fixed(optcf)_hist,rt,mustrun',
    'Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun',
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 4
ylim = [
    [0.92, 1.7],
    [0.92, 1.7],
    [0.92, 1.7],
    [0.92, 1.7],
]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue',
    'Revenue',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2   # 1.2 if using note, 1 if not
y2 = 1.07  # 1.07 if using note, 1.05 if not

gridspec_kw = {'width_ratios': [1, 2, 2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            # medianmarker='_', mediansize=10, medianfacecolor='k'
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.2))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ax[(row,col)].axhline(1, lw=0.25, c='0.5')
        
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, 1-ax track vs. fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
In [29]:
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun Revenue_track(def)/fixed(optcf)_hist,da,mustrun Revenue_track(def)/fixed(optcf)_hist,rt,mustrun Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
count 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000
mean 1.190360 1.133019 1.006021 1.283981 1.314155 1.297633 1.447377
std 0.016639 0.032371 0.033595 0.037652 0.052336 0.051749 0.131262
min 1.113813 0.888389 0.856991 1.161767 1.171260 1.166622 1.260330
2.5% 1.144030 1.040829 0.905791 1.194076 1.204142 1.201355 1.298872
25% 1.184059 1.125284 0.995113 1.265940 1.281787 1.275987 1.369893
50% 1.195275 1.144368 1.014365 1.291287 1.321106 1.301618 1.422102
75% 1.200167 1.153055 1.029195 1.308802 1.346624 1.318441 1.489774
97.5% 1.213245 1.164177 1.044061 1.341588 1.401838 1.377570 1.680171
max 1.231872 1.180118 1.061693 1.426787 1.541156 1.889847 2.660819
median
CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun Revenue_track(def)/fixed(optcf)_hist,da,mustrun Revenue_track(def)/fixed(optcf)_hist,rt,mustrun Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 1.213636 NaN 1.133700 1.158801 1.141078 1.150299 NaN 1.211987 NaN 1.133700 1.158801 1.141078 1.149981 NaN 1.195654 NaN NaN 1.150550 1.138907 1.146701 NaN 1.188600 NaN 1.124925 1.162706 1.140239 1.155738 NaN 1.177229 NaN NaN 1.165641 1.147034 1.161721 NaN 1.188673 NaN 1.124925 1.162711 1.140239 1.155748 NaN 1.186409 NaN NaN 1.171407 1.149936 1.164177 NaN
2011 1.184462 1.196981 1.107946 1.133107 1.114666 1.129048 NaN 1.180264 1.194612 1.107946 1.132935 1.114666 1.128915 NaN 1.104376 1.192087 1.106373 1.126589 1.113642 1.125715 NaN 1.179614 1.186917 1.101876 1.148362 1.121465 1.144011 NaN 1.155977 1.173355 1.105066 1.150188 1.123311 1.149328 NaN 1.180388 1.187117 1.101876 1.148452 1.121465 1.144040 NaN 1.178076 1.175964 1.105148 1.156612 1.126299 1.150336 NaN
2012 1.204967 1.181716 1.120651 1.161251 1.124788 1.152504 NaN 1.204051 1.180981 1.120549 1.161177 1.124788 1.151963 NaN 1.193566 1.177798 1.119231 1.154546 1.123634 1.150442 NaN 1.188976 1.181531 1.102166 1.166099 1.124168 1.150721 NaN 1.205031 1.171462 1.111289 1.159714 1.130362 1.155261 NaN 1.189432 1.181629 1.102182 1.166617 1.124168 1.150815 NaN 1.225690 1.173431 1.111668 1.169032 1.132100 1.155768 NaN
2013 1.179953 1.193319 1.127070 1.139756 1.137081 1.144513 NaN 1.179298 1.193181 1.127025 1.138335 1.136634 1.144482 NaN 1.164574 1.191959 1.125867 1.133703 1.135139 1.144014 NaN 1.184977 1.201803 1.058814 1.145342 1.108983 1.154610 NaN 1.184263 1.196311 1.072903 1.150395 1.126810 1.156055 NaN 1.184996 1.201955 1.058814 1.145752 1.108983 1.154622 NaN 1.197539 1.197066 1.072903 1.154342 1.130936 1.156580 NaN
2014 1.198067 1.199874 1.137230 1.161745 1.143429 1.148970 NaN 1.194885 1.199732 1.137230 1.159924 1.143429 1.148854 NaN 1.159494 1.198316 1.122012 1.157926 1.141897 1.146129 NaN 1.213929 1.203752 1.041229 1.152205 1.065022 1.116371 NaN 1.233573 1.199399 1.044929 1.159114 1.055274 1.112866 NaN 1.214147 1.203867 1.041229 1.152388 1.065022 1.116469 NaN 1.257045 1.200431 1.044991 1.169862 1.060015 1.114013 NaN
2015 1.180203 1.184606 1.137664 1.155334 1.138507 1.143015 1.163519 1.178015 1.184385 1.137359 1.152007 1.138507 1.142780 1.163519 1.105928 1.181195 1.133806 1.150349 1.136527 1.139845 1.113950 1.213394 1.187814 1.081304 1.156076 1.104415 1.136464 1.206318 1.227655 1.185337 1.100862 1.156230 1.113386 1.136088 1.203407 1.214550 1.187864 1.081320 1.158087 1.104415 1.136532 1.206318 1.279313 1.187207 1.104563 1.161805 1.115801 1.139411 1.277179
2016 1.203699 1.185931 1.135127 1.163820 1.142993 1.149273 1.186228 1.193439 1.185682 1.135127 1.162919 1.142993 1.148988 1.182417 1.074811 1.183031 1.125233 1.157975 1.140578 1.147121 1.075006 1.248827 1.174446 1.109817 1.160812 1.129348 1.145575 1.247765 1.274670 1.172251 1.130612 1.163779 1.153420 1.149887 1.276794 1.250143 1.174605 1.109817 1.161288 1.129348 1.145645 1.247888 1.349841 1.174532 1.145583 1.169550 1.158917 1.151424 1.376338
2017 1.195275 1.188038 1.119281 1.169287 1.130560 1.150807 1.182490 1.144368 1.186584 1.119269 1.169110 1.130560 1.150511 1.121469 1.014365 1.179426 1.113376 1.165342 1.128395 1.148016 0.993127 1.291287 1.181682 1.076754 1.171774 1.120036 1.145315 1.299170 1.321106 1.182709 1.092580 1.169203 1.135918 1.143157 1.348387 1.301618 1.181987 1.076754 1.171930 1.120036 1.145348 1.308847 1.422102 1.185653 1.098862 1.172432 1.141490 1.145191 1.525901
max
CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun Revenue_track(def)/fixed(optcf)_hist,da,mustrun Revenue_track(def)/fixed(optcf)_hist,rt,mustrun Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 1.245073 NaN 1.162213 1.180655 1.182804 1.200101 NaN 1.243720 NaN 1.162213 1.180655 1.182804 1.200101 NaN 1.226578 NaN NaN 1.180655 1.179590 1.197883 NaN 1.218533 NaN 1.155768 1.186720 1.184335 1.198492 NaN 1.209193 NaN NaN 1.191849 1.183515 1.200596 NaN 1.218605 NaN 1.155768 1.186720 1.184335 1.269284 NaN 1.423716 NaN NaN 1.258766 1.193524 1.716022 NaN
2011 1.228367 1.230489 1.125571 1.164497 1.158220 1.156490 NaN 1.224192 1.230489 1.125571 1.164497 1.158220 1.156490 NaN 1.145995 1.230082 1.123325 1.162620 1.155850 1.155884 NaN 1.224095 1.230381 1.123286 1.181845 1.165063 1.167081 NaN 1.197676 1.271035 1.127995 1.176035 1.159652 1.169452 NaN 1.225300 1.413759 1.123286 1.181845 1.165063 1.202461 NaN 1.302028 1.392086 1.134053 1.215248 1.163234 1.280244 NaN
2012 1.238126 1.229003 1.150921 1.196338 1.185203 1.185883 NaN 1.238126 1.229003 1.150921 1.195794 1.185203 1.185883 NaN 1.230224 1.228479 1.149848 1.189540 1.184084 1.184563 NaN 1.243719 1.384686 1.129567 1.193939 1.173515 1.211865 NaN 1.413043 1.275370 1.136601 1.191777 1.172011 1.247257 NaN 1.266654 1.904386 1.129567 1.281184 1.173515 1.212101 NaN 1.544809 1.282667 1.136601 1.580827 1.181576 1.290533 NaN
2013 1.224838 1.219486 1.147316 1.191306 1.182401 1.180713 NaN 1.224838 1.219486 1.147316 1.186844 1.182401 1.180713 NaN 1.204673 1.218594 1.145687 1.186265 1.180544 1.180499 NaN 1.252418 1.223727 1.092672 1.188060 1.171687 1.180472 NaN 1.243834 1.230533 1.100520 1.230359 1.169610 1.188343 NaN 1.252573 1.225089 1.092672 1.279305 1.171687 1.194328 NaN 1.317285 1.299695 1.100520 1.678640 1.222366 1.243708 NaN
2014 1.233165 1.237561 1.161359 1.200816 1.168225 1.171282 NaN 1.230139 1.237561 1.161359 1.200327 1.168225 1.171282 NaN 1.192237 1.235745 1.147052 1.199350 1.165909 1.170763 NaN 1.261841 1.233995 1.078632 1.216253 1.140102 1.158249 NaN 1.300952 1.246152 1.087814 1.222688 1.134068 1.160664 NaN 1.261857 1.272803 1.078632 1.219495 1.140102 1.158249 NaN 1.418395 1.277193 1.087872 1.627221 1.175926 1.267371 NaN
2015 1.215767 1.212615 1.158233 1.189473 1.165617 1.167475 1.192918 1.213018 1.212615 1.158233 1.189473 1.165617 1.167475 1.192918 1.154679 1.209214 1.154194 1.188564 1.156939 1.166487 1.146181 1.338387 1.210145 1.104741 1.198028 1.153492 1.171540 1.250215 1.361951 1.241951 1.129040 1.236867 1.159474 1.181872 1.250999 1.338387 1.218542 1.104761 1.271597 1.153492 1.285252 1.250215 1.639772 1.269242 1.133987 1.574349 1.237520 1.435168 1.373498
2016 1.236797 1.213154 1.164883 1.199435 1.187438 1.185426 1.222750 1.226526 1.213154 1.164883 1.199435 1.187438 1.185426 1.222284 1.123683 1.210166 1.151005 1.198408 1.177256 1.185426 1.124463 1.355752 1.191988 1.141944 1.187579 1.191377 1.175216 1.300944 1.438653 1.198710 1.168823 1.195777 1.205303 1.195529 1.358766 1.407147 1.249642 1.162642 1.189986 1.193383 1.179436 1.300946 1.834946 1.310014 1.229645 1.324488 1.476765 1.342671 1.607390
2017 1.231872 1.225060 1.137418 1.194131 1.169199 1.183058 1.210316 1.180118 1.225060 1.137418 1.194131 1.169199 1.183058 1.158565 1.061693 1.217070 1.131445 1.194131 1.161518 1.182520 1.076046 1.426787 1.218830 1.109250 1.196287 1.155215 1.176106 1.346755 1.541156 1.242655 1.126912 1.199620 1.163344 1.185199 1.728387 1.889847 1.266207 1.274044 1.230165 1.155215 1.176106 1.379000 2.660819 1.584219 1.546495 1.464246 1.379264 1.240983 2.703056
CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.205674 1.182921 1.199947 1.179000 1.195210 1.178027 1.197063 1.190360 1.197914 1.183841 1.194293 1.199710 1.184308 1.186799 1.188641 1.136201 1.107736 1.122396 1.127026 1.134952 1.135156 1.135737 1.119598 1.156711 1.133030 1.156411 1.141178 1.160746 1.155958 1.162685 1.164538 1.147908 1.120182 1.137409 1.145147 1.144969 1.140727 1.152161 1.133892 1.156194 1.127473 1.147861 1.146251 1.149137 1.143271 1.149060 1.148573 1.161050 1.184067 1.176863
std 0.024422 0.023558 0.021722 0.019730 0.018567 0.020008 0.019118 0.016639 0.010385 0.009552 0.006582 0.009706 0.010552 0.007280 0.010146 0.008307 0.006472 0.007869 0.007290 0.009585 0.007628 0.006774 0.006926 0.013750 0.012781 0.014265 0.013471 0.023050 0.019664 0.014453 0.018491 0.015657 0.015037 0.020630 0.015148 0.007061 0.007513 0.015460 0.012040 0.015333 0.009066 0.015573 0.008908 0.008094 0.006675 0.009019 0.012139 0.011985 0.013895 0.027657
min 1.121335 1.087633 1.091697 1.101150 1.118839 1.097817 1.126527 1.113813 1.177764 1.168385 1.180782 1.175886 1.163198 1.170989 1.168906 1.121885 1.085560 1.106063 1.105548 1.105512 1.115861 1.122775 1.099711 1.104690 1.101493 1.122662 1.110892 1.095983 1.112748 1.098842 1.076367 1.129650 1.096421 1.114237 1.121225 1.120487 1.121091 1.130319 1.109137 1.130170 1.101953 1.116814 1.128458 1.129527 1.123929 1.131858 1.122993 1.109144 1.119223 1.091099
2.5% 1.143506 1.124217 1.141994 1.130434 1.146722 1.128067 1.143370 1.144030 1.182810 1.171761 1.184687 1.183169 1.168423 1.174768 1.173613 1.126297 1.090317 1.110608 1.110233 1.110364 1.117911 1.125390 1.104458 1.128222 1.106128 1.127332 1.119259 1.113988 1.117357 1.131585 1.117621 1.133780 1.101387 1.118800 1.129669 1.128048 1.126541 1.135767 1.114105 1.135364 1.107491 1.124651 1.132583 1.134321 1.131716 1.135102 1.127282 1.128196 1.144799 1.105300
25% 1.189496 1.171526 1.189998 1.169169 1.188757 1.169328 1.187897 1.184059 1.189325 1.175997 1.189014 1.191575 1.174682 1.181693 1.179739 1.130547 1.105556 1.117207 1.123158 1.130355 1.129954 1.130833 1.115516 1.148290 1.126247 1.148263 1.133001 1.146117 1.140261 1.151110 1.155526 1.135823 1.108858 1.120273 1.134159 1.142751 1.137128 1.141340 1.128710 1.143615 1.122823 1.132028 1.139657 1.142976 1.138795 1.141381 1.138414 1.157126 1.179512 1.169976
50% 1.213636 1.184462 1.204967 1.179953 1.198067 1.180203 1.203699 1.195275 1.196981 1.181716 1.193319 1.199874 1.184606 1.185931 1.188038 1.133700 1.107946 1.120651 1.127070 1.137230 1.137664 1.135127 1.119281 1.158801 1.133107 1.161251 1.139756 1.161745 1.155334 1.163820 1.169287 1.141078 1.114666 1.124788 1.137081 1.143429 1.138507 1.142993 1.130560 1.150299 1.129048 1.152504 1.144513 1.148970 1.143015 1.149273 1.150807 1.163519 1.186228 1.182490
75% 1.223951 1.202201 1.215094 1.193146 1.207661 1.192561 1.209913 1.200167 1.205482 1.190184 1.198580 1.206984 1.191806 1.190603 1.196341 1.138461 1.111079 1.125147 1.130902 1.140678 1.140630 1.139222 1.124283 1.166592 1.143270 1.165895 1.146970 1.181627 1.176484 1.175114 1.175762 1.158095 1.128693 1.157609 1.159073 1.150100 1.145600 1.168214 1.139408 1.171907 1.133522 1.160954 1.152330 1.155033 1.146711 1.154591 1.157689 1.168028 1.191293 1.199460
97.5% 1.237011 1.216968 1.227870 1.211240 1.222818 1.207223 1.222281 1.213245 1.220786 1.202480 1.207922 1.216214 1.204522 1.204361 1.208362 1.158043 1.119779 1.140370 1.143867 1.148652 1.144836 1.152181 1.133085 1.175680 1.154311 1.176664 1.176441 1.192276 1.185548 1.181925 1.191386 1.179822 1.153446 1.180752 1.178504 1.156633 1.156839 1.184089 1.162578 1.184132 1.143226 1.174247 1.164784 1.165046 1.159510 1.170354 1.169076 1.176691 1.205888 1.203981
max 1.245073 1.228367 1.238126 1.224838 1.233165 1.215767 1.236797 1.231872 1.230489 1.229003 1.219486 1.237561 1.212615 1.213154 1.225060 1.162213 1.125571 1.150921 1.147316 1.161359 1.158233 1.164883 1.137418 1.180655 1.164497 1.196338 1.191306 1.200816 1.189473 1.199435 1.194131 1.182804 1.158220 1.185203 1.182401 1.168225 1.165617 1.187438 1.169199 1.200101 1.156490 1.185883 1.180713 1.171282 1.167475 1.185426 1.183058 1.192918 1.222750 1.210316
CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.204529 1.179162 1.199145 1.178703 1.191829 1.174915 1.188556 1.133019 1.195355 1.182805 1.194073 1.199351 1.184094 1.186263 1.187475 1.136169 1.107723 1.122338 1.127000 1.134916 1.134973 1.135636 1.118875 1.156011 1.132513 1.153566 1.138641 1.158923 1.154248 1.161916 1.163659 1.147900 1.120182 1.137395 1.144948 1.144969 1.140668 1.152067 1.133856 1.155763 1.127355 1.147518 1.146115 1.148971 1.142986 1.148797 1.148411 1.161040 1.182093 1.115139
std 0.024238 0.023983 0.022216 0.019625 0.018371 0.019282 0.021344 0.032371 0.010971 0.012465 0.006744 0.009927 0.010498 0.007767 0.011217 0.008340 0.006504 0.007883 0.007305 0.009581 0.007684 0.006713 0.009343 0.014672 0.013208 0.020831 0.017619 0.024949 0.020972 0.015142 0.018738 0.015662 0.015037 0.020636 0.015276 0.007061 0.007439 0.015352 0.012024 0.015783 0.009178 0.015783 0.009202 0.008238 0.007065 0.009332 0.012174 0.011976 0.013936 0.019962
min 1.120788 1.079850 1.085268 1.100658 1.114405 1.097817 1.111950 0.888389 1.028827 0.884082 1.149327 1.151961 1.161977 1.137293 1.081241 1.121885 1.085560 1.106063 1.105548 1.105512 1.115861 1.122775 1.017208 1.090396 1.101230 1.012355 1.031510 1.013057 1.092556 1.098334 1.076367 1.129650 1.096421 1.114237 1.115666 1.120487 1.121091 1.130319 1.109137 1.074097 1.077102 1.076390 1.090193 1.117569 1.067044 1.092217 1.114946 1.109144 1.119223 1.048456
2.5% 1.143038 1.121569 1.141994 1.130434 1.143149 1.128067 1.138832 1.040829 1.179028 1.171048 1.184064 1.181719 1.168358 1.173434 1.171708 1.125724 1.090036 1.109693 1.110233 1.110364 1.117911 1.125348 1.102124 1.121518 1.103481 1.114263 1.112008 1.108453 1.113518 1.125337 1.117587 1.133780 1.101387 1.118800 1.129481 1.128048 1.126541 1.135255 1.114105 1.134336 1.107376 1.124016 1.132517 1.134310 1.130949 1.134766 1.127271 1.128196 1.144330 1.060729
25% 1.188635 1.167883 1.189242 1.168829 1.185477 1.166079 1.177383 1.125284 1.188061 1.175513 1.188836 1.191522 1.174580 1.180944 1.178372 1.130547 1.105556 1.117207 1.123158 1.130355 1.129743 1.130761 1.115211 1.146790 1.126147 1.144928 1.131693 1.145858 1.139857 1.150470 1.153129 1.135823 1.108858 1.120273 1.134159 1.142751 1.137128 1.141340 1.128676 1.143163 1.122625 1.131819 1.139582 1.142761 1.138592 1.141188 1.138109 1.157089 1.176190 1.115759
50% 1.211987 1.180264 1.204051 1.179298 1.194885 1.178015 1.193439 1.144368 1.194612 1.180981 1.193181 1.199732 1.184385 1.185682 1.186584 1.133700 1.107946 1.120549 1.127025 1.137230 1.137359 1.135127 1.119269 1.158801 1.132935 1.161177 1.138335 1.159924 1.152007 1.162919 1.169110 1.141078 1.114666 1.124788 1.136634 1.143429 1.138507 1.142993 1.130560 1.149981 1.128915 1.151963 1.144482 1.148854 1.142780 1.148988 1.150511 1.163519 1.182417 1.121469
75% 1.222805 1.198484 1.214829 1.192373 1.204099 1.189843 1.204682 1.153055 1.202788 1.189247 1.198355 1.206737 1.191586 1.190224 1.196128 1.138408 1.111079 1.124892 1.130902 1.140543 1.140559 1.139222 1.124107 1.166315 1.143270 1.165895 1.145713 1.181191 1.175697 1.174457 1.174458 1.158095 1.128693 1.157609 1.159073 1.150100 1.145369 1.168214 1.139408 1.171789 1.133405 1.160856 1.152330 1.155026 1.146532 1.154404 1.157427 1.168018 1.190338 1.124638
97.5% 1.235927 1.214099 1.227146 1.210526 1.218021 1.200216 1.215746 1.164177 1.213279 1.201280 1.207819 1.216173 1.204424 1.204218 1.208354 1.158043 1.119779 1.140370 1.143867 1.148652 1.144810 1.152076 1.133085 1.175041 1.154311 1.176201 1.176441 1.192276 1.185548 1.181713 1.191386 1.179822 1.153446 1.180752 1.178504 1.156633 1.156121 1.183213 1.162578 1.184132 1.143226 1.174146 1.164784 1.165046 1.159510 1.170101 1.169076 1.176691 1.205603 1.143960
max 1.243720 1.224192 1.238126 1.224838 1.230139 1.213018 1.226526 1.180118 1.230489 1.229003 1.219486 1.237561 1.212615 1.213154 1.225060 1.162213 1.125571 1.150921 1.147316 1.161359 1.158233 1.164883 1.137418 1.180655 1.164497 1.195794 1.186844 1.200327 1.189473 1.199435 1.194131 1.182804 1.158220 1.185203 1.182401 1.168225 1.165617 1.187438 1.169199 1.200101 1.156490 1.185883 1.180713 1.171282 1.167475 1.185426 1.183058 1.192918 1.222284 1.158565
CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.189025 1.102366 1.188702 1.163206 1.155698 1.088590 1.058886 1.006021 1.181391 1.174879 1.191812 1.197810 1.181147 1.180402 1.177827 NaN 1.106136 1.120949 1.125358 1.120891 1.131402 1.125259 1.110617 1.147151 1.122249 1.145595 1.128662 1.152964 1.148264 1.155995 1.159107 1.144980 1.118223 1.136256 1.141653 1.141546 1.134748 1.139543 1.124261 1.152743 1.124727 1.145859 1.145371 1.146326 1.139110 1.146444 1.146156 1.111360 1.066702 1.003447
std 0.026140 0.020379 0.022798 0.018983 0.017148 0.045331 0.037954 0.033595 0.038733 0.020365 0.007400 0.010150 0.011277 0.011917 0.022690 NaN 0.006360 0.007996 0.008030 0.008113 0.007740 0.007204 0.015917 0.019018 0.023053 0.026836 0.029680 0.031360 0.024818 0.019750 0.021955 0.014911 0.014708 0.020165 0.018399 0.010416 0.012275 0.014132 0.019897 0.016565 0.009498 0.016600 0.009783 0.009457 0.010316 0.011193 0.013337 0.013861 0.025168 0.025207
min 0.871278 1.023426 1.069489 1.082929 1.075824 0.980093 0.934079 0.856991 1.012902 0.946214 1.112109 1.149868 1.156994 1.122016 0.871683 NaN 1.083449 1.104467 1.095355 1.095401 1.111452 1.098569 1.001684 1.091744 1.023111 0.981707 0.941612 0.924574 1.036037 1.072700 1.037790 1.125375 1.087809 1.114237 1.064776 1.082820 1.069806 1.076009 1.033365 1.075188 1.090428 1.070664 1.088281 1.069534 1.050763 1.077570 1.095061 1.058853 0.994041 0.878038
2.5% 1.132671 1.052230 1.133768 1.117694 1.107624 1.005251 0.987883 0.905791 1.056149 1.118297 1.179045 1.180961 1.162550 1.143031 1.126560 NaN 1.089282 1.107922 1.105428 1.099485 1.114434 1.112906 1.065619 1.112144 1.061928 1.076769 1.046179 1.075748 1.090821 1.104353 1.107945 1.129608 1.098420 1.118202 1.091877 1.105541 1.091808 1.094868 1.050550 1.120034 1.105676 1.118874 1.129771 1.130032 1.120846 1.128572 1.122146 1.071083 1.027922 0.966158
25% 1.175651 1.091149 1.176971 1.152902 1.149150 1.044745 1.025620 0.995113 1.184190 1.172192 1.187320 1.189981 1.170801 1.177701 1.172594 NaN 1.103920 1.115506 1.121701 1.118043 1.126284 1.120221 1.107498 1.131756 1.112072 1.136082 1.123247 1.141951 1.136050 1.146016 1.148505 1.133735 1.108353 1.119975 1.133761 1.139954 1.131849 1.136840 1.122439 1.141475 1.118477 1.129928 1.138802 1.139733 1.134074 1.138279 1.135218 1.106169 1.040254 0.989202
50% 1.195654 1.104376 1.193566 1.164574 1.159494 1.105928 1.074811 1.014365 1.192087 1.177798 1.191959 1.198316 1.181195 1.183031 1.179426 NaN 1.106373 1.119231 1.125867 1.122012 1.133806 1.125233 1.113376 1.150550 1.126589 1.154546 1.133703 1.157926 1.150349 1.157975 1.165342 1.138907 1.113642 1.123634 1.135139 1.141897 1.136527 1.140578 1.128395 1.146701 1.125715 1.150442 1.144014 1.146129 1.139845 1.147121 1.148016 1.113950 1.075006 0.993127
75% 1.206106 1.117134 1.205331 1.177460 1.166578 1.127535 1.092994 1.029195 1.200978 1.184322 1.196060 1.205163 1.190367 1.186929 1.193776 NaN 1.109600 1.123784 1.129315 1.125343 1.136998 1.129334 1.118929 1.161798 1.138650 1.162892 1.142679 1.176584 1.169860 1.172109 1.171795 1.151636 1.125375 1.156411 1.154998 1.147233 1.140934 1.143326 1.131420 1.169666 1.131619 1.159576 1.151910 1.153367 1.144604 1.153107 1.155893 1.120561 1.086705 1.023757
97.5% 1.218310 1.134244 1.217099 1.192547 1.179406 1.147901 1.104884 1.044061 1.212859 1.198513 1.206055 1.215055 1.201354 1.195658 1.204638 NaN 1.117626 1.139319 1.142357 1.132611 1.141159 1.143305 1.127765 1.174005 1.150487 1.174727 1.175116 1.190190 1.183106 1.181234 1.189887 1.176233 1.149954 1.180181 1.176078 1.154360 1.150019 1.169545 1.151111 1.182641 1.141430 1.173405 1.164520 1.163896 1.158534 1.169578 1.168605 1.129871 1.108533 1.050959
max 1.226578 1.145995 1.230224 1.204673 1.192237 1.154679 1.123683 1.061693 1.230082 1.228479 1.218594 1.235745 1.209214 1.210166 1.217070 NaN 1.123325 1.149848 1.145687 1.147052 1.154194 1.151005 1.131445 1.180655 1.162620 1.189540 1.186265 1.199350 1.188564 1.198408 1.194131 1.179590 1.155850 1.184084 1.180544 1.165909 1.156939 1.177256 1.161518 1.197883 1.155884 1.184563 1.180499 1.170763 1.166487 1.185426 1.182520 1.146181 1.124463 1.076046
Revenue_track(def)/fixed(optcf)_hist,da,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.181917 1.178839 1.185226 1.183955 1.214425 1.215919 1.244379 1.283981 1.187774 1.182895 1.202573 1.202635 1.187485 1.174135 1.181507 1.127146 1.101453 1.103054 1.060832 1.041260 1.076714 1.109886 1.076873 1.157579 1.147464 1.163556 1.145376 1.154716 1.159442 1.158680 1.165527 1.144735 1.126639 1.128722 1.119979 1.075455 1.106741 1.141804 1.119153 1.159325 1.142768 1.147064 1.153731 1.111158 1.135407 1.145694 1.142384 1.204890 1.244522 1.286184
std 0.022381 0.023081 0.026295 0.020538 0.022739 0.030419 0.031715 0.037652 0.008695 0.012378 0.005544 0.009585 0.007014 0.005457 0.007369 0.009825 0.008149 0.008444 0.014137 0.010555 0.011574 0.007588 0.013086 0.016710 0.013108 0.013659 0.014171 0.035206 0.020213 0.013709 0.019900 0.018879 0.015191 0.020859 0.026342 0.022043 0.016630 0.023355 0.017076 0.014434 0.007885 0.012586 0.008340 0.020596 0.011897 0.007030 0.011863 0.014780 0.019459 0.037499
min 1.110268 1.085192 1.069278 1.104117 1.132645 1.140847 1.139030 1.161767 1.140965 1.117358 1.178982 1.152874 1.165554 1.144097 1.138525 1.108089 1.073062 1.078627 1.022068 1.009493 1.045780 1.095654 1.028590 1.093155 1.112657 1.127029 1.104380 1.068294 1.109238 1.094050 1.068217 1.105689 1.099962 1.099054 1.074313 1.028457 1.069932 1.110392 1.074899 1.125543 1.102139 1.117784 1.122668 1.049814 1.091526 1.115595 1.107168 1.137132 1.146840 1.169234
2.5% 1.126661 1.120422 1.126192 1.135459 1.161462 1.157936 1.161781 1.194076 1.172540 1.169058 1.194072 1.183727 1.175063 1.163306 1.168623 1.113803 1.078222 1.086865 1.029663 1.018334 1.051404 1.098115 1.047585 1.120548 1.120203 1.135910 1.118632 1.091836 1.126835 1.125884 1.117741 1.121377 1.104204 1.104092 1.081831 1.042282 1.071731 1.112174 1.082637 1.136393 1.124865 1.126038 1.135481 1.066365 1.111233 1.132726 1.119322 1.170746 1.198473 1.188146
25% 1.167852 1.167889 1.168783 1.173048 1.202742 1.195185 1.224837 1.265940 1.182747 1.178228 1.198743 1.195959 1.182244 1.171368 1.177249 1.120303 1.099305 1.097383 1.052952 1.036918 1.069549 1.103741 1.070207 1.150212 1.139108 1.153661 1.139538 1.122667 1.142036 1.149617 1.153923 1.129585 1.117118 1.109287 1.101258 1.062077 1.099321 1.123670 1.113603 1.147931 1.138369 1.134901 1.148977 1.099807 1.128734 1.140771 1.132811 1.197144 1.236193 1.277404
50% 1.188600 1.179614 1.188976 1.184977 1.213929 1.213394 1.248827 1.291287 1.186917 1.181531 1.201803 1.203752 1.187814 1.174446 1.181682 1.124925 1.101876 1.102166 1.058814 1.041229 1.081304 1.109817 1.076754 1.162706 1.148362 1.166099 1.145342 1.152205 1.156076 1.160812 1.171774 1.140239 1.121465 1.124168 1.108983 1.065022 1.104415 1.129348 1.120036 1.155738 1.144011 1.150721 1.154610 1.116371 1.136464 1.145575 1.145315 1.206318 1.247765 1.299170
75% 1.199382 1.196324 1.205633 1.200130 1.232974 1.241071 1.266993 1.308802 1.191289 1.185908 1.205589 1.209090 1.192114 1.177406 1.185309 1.131823 1.105781 1.108163 1.069954 1.047729 1.084889 1.114889 1.086376 1.168968 1.157750 1.173450 1.151059 1.185843 1.179784 1.169377 1.179369 1.159060 1.135580 1.146621 1.146868 1.090156 1.119276 1.165085 1.127829 1.172926 1.148169 1.157647 1.159238 1.126602 1.141845 1.149953 1.151302 1.215337 1.258016 1.312071
97.5% 1.211921 1.213284 1.224514 1.215376 1.251071 1.265296 1.296627 1.341588 1.210202 1.204802 1.214694 1.219197 1.201028 1.183961 1.197186 1.154044 1.115509 1.121290 1.088744 1.059851 1.090714 1.125500 1.101213 1.178468 1.168269 1.185894 1.180674 1.207165 1.192289 1.176136 1.189863 1.182637 1.158439 1.168053 1.168654 1.123232 1.136183 1.184847 1.147870 1.184392 1.154991 1.166954 1.168214 1.140186 1.161733 1.160864 1.162110 1.227137 1.273545 1.330375
max 1.218533 1.224095 1.243719 1.252418 1.261841 1.338387 1.355752 1.426787 1.230381 1.384686 1.223727 1.233995 1.210145 1.191988 1.218830 1.155768 1.123286 1.129567 1.092672 1.078632 1.104741 1.141944 1.109250 1.186720 1.181845 1.193939 1.188060 1.216253 1.198028 1.187579 1.196287 1.184335 1.165063 1.173515 1.171687 1.140102 1.153492 1.191377 1.155215 1.198492 1.167081 1.211865 1.180472 1.158249 1.171540 1.175216 1.176106 1.250215 1.300944 1.346755
Revenue_track(def)/fixed(optcf)_hist,rt,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.169654 1.154341 1.208358 1.184296 1.233457 1.231424 1.270990 1.314155 1.173679 1.173916 1.196387 1.197151 1.185748 1.171324 1.182024 NaN 1.104696 1.112299 1.072420 1.046171 1.095428 1.131191 1.089367 1.160614 1.148633 1.158114 1.148857 1.161811 1.161352 1.162536 1.161656 1.147699 1.127997 1.136101 1.129810 1.068539 1.116495 1.155259 1.129516 1.164757 1.149264 1.152389 1.155133 1.106295 1.136208 1.150129 1.140551 1.202060 1.271793 1.303549
std 0.024551 0.021519 0.054546 0.023076 0.026786 0.035793 0.042620 0.052336 0.008318 0.016602 0.006015 0.012716 0.011086 0.007551 0.010428 NaN 0.009310 0.008191 0.015197 0.010893 0.013029 0.008582 0.018026 0.015180 0.012179 0.014574 0.016503 0.031269 0.023926 0.013250 0.019064 0.015977 0.012438 0.013231 0.021404 0.023542 0.017389 0.020392 0.022699 0.013102 0.006236 0.012919 0.010051 0.020377 0.011963 0.007382 0.014120 0.014677 0.041665 0.102732
min 1.006985 1.071476 1.042955 1.091650 1.147781 1.140549 1.153979 1.171260 1.142763 1.096398 1.173103 1.120406 1.144171 1.117077 1.126516 NaN 1.074328 1.085771 1.023092 1.017849 1.063995 1.112637 1.018506 1.105374 1.113477 1.125920 1.105038 1.075332 1.118396 1.096191 1.063944 1.112800 1.090459 1.110625 1.076052 1.020760 1.076817 1.122341 1.068640 1.100543 1.113560 1.110421 1.113949 1.043976 1.073069 1.093460 1.084628 1.140601 1.145627 1.069073
2.5% 1.109924 1.105358 1.114778 1.131957 1.173938 1.157547 1.178197 1.204142 1.159496 1.142274 1.184788 1.168424 1.165726 1.155328 1.159218 NaN 1.079061 1.093316 1.034982 1.024174 1.069512 1.116459 1.046464 1.128266 1.120357 1.129197 1.119101 1.105837 1.127902 1.131637 1.117100 1.129621 1.106916 1.117195 1.085630 1.033193 1.078209 1.127177 1.080325 1.144490 1.136695 1.130513 1.131459 1.066072 1.114611 1.136962 1.112858 1.164553 1.189393 1.088072
25% 1.153125 1.142573 1.174226 1.172601 1.217273 1.208121 1.241875 1.281787 1.168915 1.167611 1.192499 1.190561 1.178280 1.168170 1.177065 NaN 1.101979 1.108413 1.066753 1.039922 1.084421 1.125024 1.080933 1.150384 1.141119 1.149191 1.139479 1.137470 1.142381 1.154622 1.150284 1.133923 1.121321 1.125005 1.114247 1.050970 1.106604 1.140869 1.117715 1.155709 1.145518 1.140709 1.149404 1.089789 1.129420 1.145704 1.130677 1.194390 1.245417 1.276665
50% 1.177229 1.155977 1.205031 1.184263 1.233573 1.227655 1.274670 1.321106 1.173355 1.171462 1.196311 1.199399 1.185337 1.172251 1.182709 NaN 1.105066 1.111289 1.072903 1.044929 1.100862 1.130612 1.092580 1.165641 1.150188 1.159714 1.150395 1.159114 1.156230 1.163779 1.169203 1.147034 1.123311 1.130362 1.126810 1.055274 1.113386 1.153420 1.135918 1.161721 1.149328 1.155261 1.156055 1.112866 1.136088 1.149887 1.143157 1.203407 1.276794 1.348387
75% 1.187771 1.170526 1.229244 1.199650 1.252196 1.257081 1.296987 1.346624 1.177926 1.176793 1.200201 1.205410 1.191938 1.175402 1.186882 NaN 1.110385 1.116882 1.081059 1.053966 1.105559 1.136526 1.101839 1.170971 1.156911 1.167118 1.155590 1.183938 1.180108 1.172579 1.174424 1.157568 1.136992 1.145586 1.145472 1.088729 1.131117 1.169705 1.146466 1.175863 1.153347 1.163316 1.161215 1.122066 1.142615 1.154550 1.151247 1.212967 1.306846 1.369944
97.5% 1.200277 1.188287 1.359989 1.225336 1.283092 1.299632 1.348842 1.401838 1.188946 1.216728 1.208436 1.214098 1.208614 1.184376 1.201019 NaN 1.118901 1.128558 1.097478 1.066718 1.111466 1.149157 1.118047 1.179885 1.169412 1.186294 1.182577 1.215038 1.218906 1.183850 1.186180 1.180483 1.153047 1.164782 1.167830 1.113029 1.145731 1.198272 1.161728 1.188348 1.160981 1.172055 1.173438 1.134513 1.161905 1.164451 1.163397 1.224471 1.330041 1.413639
max 1.209193 1.197676 1.413043 1.243834 1.300952 1.361951 1.438653 1.541156 1.271035 1.275370 1.230533 1.246152 1.241951 1.198710 1.242655 NaN 1.127995 1.136601 1.100520 1.087814 1.129040 1.168823 1.126912 1.191849 1.176035 1.191777 1.230359 1.222688 1.236867 1.195777 1.199620 1.183515 1.159652 1.172011 1.169610 1.134068 1.159474 1.205303 1.163344 1.200596 1.169452 1.247257 1.188343 1.160664 1.181872 1.195529 1.185199 1.250999 1.358766 1.728387
Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.182043 1.179648 1.186015 1.184061 1.214618 1.216763 1.246307 1.297633 1.188637 1.183512 1.202692 1.203048 1.187615 1.174828 1.182178 1.127156 1.101453 1.103063 1.060845 1.041266 1.076749 1.109920 1.077146 1.157944 1.147570 1.165519 1.147275 1.155698 1.160632 1.159049 1.166140 1.144736 1.126639 1.128729 1.120038 1.075455 1.106756 1.141818 1.119160 1.159573 1.142848 1.147216 1.153808 1.111247 1.135602 1.145810 1.142466 1.204891 1.244844 1.296076
std 0.022436 0.022878 0.026940 0.020555 0.022758 0.030341 0.033055 0.051749 0.011987 0.021364 0.005614 0.010297 0.007131 0.006827 0.008244 0.009816 0.008149 0.008451 0.014137 0.010559 0.011565 0.007752 0.014352 0.016670 0.013128 0.016587 0.019464 0.035485 0.021923 0.013660 0.020355 0.018878 0.015191 0.020855 0.026377 0.022043 0.016655 0.023383 0.017083 0.014958 0.008039 0.012701 0.008420 0.020533 0.012298 0.007105 0.011895 0.014781 0.019637 0.040425
min 1.110286 1.089072 1.072294 1.104159 1.132682 1.140847 1.139030 1.166622 1.166298 1.121188 1.179144 1.152874 1.165554 1.144097 1.138525 1.108089 1.073062 1.078627 1.022068 1.009493 1.045806 1.095654 1.034497 1.093155 1.112779 1.134035 1.104380 1.068294 1.109246 1.094050 1.068217 1.105689 1.099962 1.099054 1.074313 1.028457 1.069932 1.110392 1.074899 1.125543 1.102139 1.117784 1.122668 1.049814 1.091526 1.118942 1.107168 1.137132 1.146840 1.174869
2.5% 1.126724 1.120590 1.126192 1.135459 1.161889 1.158449 1.161781 1.201355 1.174043 1.169548 1.194238 1.183727 1.175075 1.164058 1.169017 1.113803 1.078222 1.086865 1.029663 1.018334 1.051425 1.098115 1.048867 1.120548 1.120204 1.136947 1.118632 1.092225 1.126852 1.126827 1.117911 1.121377 1.104204 1.104092 1.081831 1.042282 1.071731 1.112174 1.082637 1.136403 1.124865 1.126156 1.135501 1.066365 1.111233 1.132758 1.119421 1.170746 1.198474 1.194159
25% 1.167908 1.169092 1.168796 1.173148 1.202881 1.196596 1.225445 1.275987 1.183082 1.178367 1.198796 1.196061 1.182304 1.171603 1.177390 1.120303 1.099305 1.097383 1.052953 1.036918 1.069903 1.103741 1.070228 1.150233 1.139163 1.155952 1.139616 1.122667 1.142576 1.150155 1.154145 1.129585 1.117118 1.109287 1.101258 1.062077 1.099321 1.123670 1.113603 1.147970 1.138383 1.134991 1.148977 1.100144 1.128937 1.140867 1.132978 1.197144 1.236200 1.284325
50% 1.188673 1.180388 1.189432 1.184996 1.214147 1.214550 1.250143 1.301618 1.187117 1.181629 1.201955 1.203867 1.187864 1.174605 1.181987 1.124925 1.101876 1.102182 1.058814 1.041229 1.081320 1.109817 1.076754 1.162711 1.148452 1.166617 1.145752 1.152388 1.158087 1.161288 1.171930 1.140239 1.121465 1.124168 1.108983 1.065022 1.104415 1.129348 1.120036 1.155748 1.144040 1.150815 1.154622 1.116469 1.136532 1.145645 1.145348 1.206318 1.247888 1.308847
75% 1.199501 1.196914 1.206448 1.200204 1.233217 1.241513 1.269006 1.318441 1.191564 1.186251 1.205655 1.209477 1.192135 1.177604 1.185677 1.131823 1.105781 1.108163 1.070366 1.047729 1.084905 1.114889 1.086376 1.169065 1.158317 1.173687 1.152219 1.186845 1.179868 1.169377 1.179844 1.159060 1.135580 1.146621 1.146868 1.090156 1.119276 1.165085 1.127829 1.173245 1.148255 1.157762 1.159284 1.126674 1.141921 1.150076 1.151367 1.215337 1.258657 1.324756
97.5% 1.211997 1.214114 1.232054 1.215376 1.251221 1.266347 1.301310 1.377570 1.210589 1.205101 1.215214 1.220971 1.201152 1.186844 1.199451 1.154044 1.115509 1.121290 1.088744 1.059851 1.090727 1.125500 1.101213 1.178617 1.168693 1.189270 1.181707 1.207165 1.196373 1.176659 1.193189 1.182637 1.158439 1.168053 1.168654 1.123232 1.136316 1.184847 1.147870 1.185050 1.155127 1.167419 1.168509 1.140186 1.162369 1.161283 1.162441 1.227137 1.273545 1.343914
max 1.218605 1.225300 1.266654 1.252573 1.261857 1.338387 1.407147 1.889847 1.413759 1.904386 1.225089 1.272803 1.218542 1.249642 1.266207 1.155768 1.123286 1.129567 1.092672 1.078632 1.104761 1.162642 1.274044 1.186720 1.181845 1.281184 1.279305 1.219495 1.271597 1.189986 1.230165 1.184335 1.165063 1.173515 1.171687 1.140102 1.153492 1.193383 1.155215 1.269284 1.202461 1.212101 1.194328 1.158249 1.285252 1.179436 1.176106 1.250215 1.300946 1.379000
Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.178439 1.177863 1.232668 1.200964 1.264744 1.329904 1.364294 1.447377 1.179093 1.178177 1.198053 1.198947 1.188937 1.175484 1.190139 NaN 1.104925 1.112464 1.072453 1.046351 1.099406 1.146547 1.099272 1.168344 1.155910 1.172304 1.164316 1.171963 1.171236 1.170415 1.168777 1.153040 1.130702 1.138563 1.133497 1.071333 1.121176 1.180530 1.146732 1.167541 1.150573 1.153609 1.155887 1.108226 1.140905 1.152678 1.144233 1.275783 1.389333 1.553425
std 0.027015 0.030475 0.057318 0.028671 0.037703 0.108091 0.070678 0.131262 0.014328 0.017017 0.008258 0.013661 0.014106 0.011341 0.023930 NaN 0.009608 0.008220 0.015134 0.010889 0.012958 0.011298 0.024892 0.019234 0.015771 0.041091 0.061852 0.047876 0.043002 0.021388 0.029355 0.018729 0.013241 0.014282 0.024717 0.024995 0.021854 0.065404 0.045681 0.018812 0.008011 0.014709 0.010495 0.021537 0.016356 0.012042 0.012522 0.017688 0.047597 0.253418
min 1.100273 1.084651 1.121712 1.111368 1.163002 1.167293 1.218189 1.260330 1.151498 1.122557 1.178177 1.121664 1.144595 1.117327 1.126555 NaN 1.074328 1.085787 1.023394 1.018355 1.069441 1.127823 1.051757 1.107984 1.126440 1.126247 1.106232 1.079000 1.119753 1.101725 1.071013 1.114136 1.091101 1.110951 1.077105 1.021930 1.078370 1.127029 1.075562 1.101132 1.113601 1.110514 1.114429 1.044128 1.077368 1.102866 1.103553 1.208960 1.260082 1.178964
2.5% 1.117229 1.115872 1.148914 1.148406 1.195190 1.203775 1.237562 1.298872 1.163267 1.157703 1.186528 1.174288 1.167959 1.157496 1.162582 NaN 1.079061 1.093329 1.035135 1.024595 1.073440 1.131499 1.063614 1.135605 1.130110 1.134388 1.120300 1.112993 1.130946 1.141796 1.125538 1.130803 1.109475 1.117580 1.088213 1.033971 1.079762 1.130648 1.087624 1.145915 1.136771 1.131594 1.132567 1.068281 1.120397 1.138247 1.122132 1.236337 1.313398 1.199461
25% 1.159752 1.159195 1.194247 1.186304 1.239815 1.245347 1.321760 1.369893 1.171484 1.169235 1.193315 1.192022 1.180092 1.170818 1.179665 NaN 1.101979 1.108559 1.066753 1.040034 1.088374 1.139281 1.089075 1.159510 1.144895 1.157308 1.145069 1.141612 1.146545 1.158134 1.154253 1.136158 1.122927 1.126308 1.114778 1.052257 1.108357 1.143558 1.121861 1.156839 1.146429 1.141269 1.149915 1.090831 1.132708 1.147539 1.134384 1.267693 1.352741 1.381610
50% 1.186409 1.178076 1.225690 1.197539 1.257045 1.279313 1.349841 1.422102 1.175964 1.173431 1.197066 1.200431 1.187207 1.174532 1.185653 NaN 1.105148 1.111668 1.072903 1.044991 1.104563 1.145583 1.098862 1.171407 1.156612 1.169032 1.154342 1.169862 1.161805 1.169550 1.172432 1.149936 1.126299 1.132100 1.130936 1.060015 1.115801 1.158917 1.141490 1.164177 1.150336 1.155768 1.156580 1.114013 1.139411 1.151424 1.145191 1.277179 1.376338 1.525901
75% 1.197607 1.195866 1.250536 1.214697 1.292704 1.408467 1.391693 1.489774 1.182898 1.181590 1.201352 1.206795 1.194422 1.178572 1.196103 NaN 1.110620 1.117304 1.081059 1.054111 1.109681 1.150463 1.107311 1.176549 1.163593 1.178661 1.163972 1.195431 1.186416 1.176941 1.178334 1.166455 1.138550 1.149683 1.151019 1.091878 1.136092 1.194176 1.159051 1.178559 1.154446 1.163891 1.161792 1.122882 1.146431 1.156309 1.152424 1.284167 1.431716 1.806245
97.5% 1.210492 1.236665 1.391501 1.268023 1.347535 1.582260 1.520961 1.680171 1.210388 1.227332 1.216783 1.219348 1.226082 1.198987 1.236174 NaN 1.121525 1.128558 1.097478 1.066789 1.115263 1.179902 1.131562 1.201694 1.196771 1.238451 1.249975 1.246872 1.237129 1.223418 1.208823 1.189883 1.157949 1.166976 1.193062 1.115763 1.167620 1.432421 1.323875 1.193098 1.164742 1.175531 1.175892 1.140910 1.168466 1.172054 1.169777 1.312614 1.470020 1.978326
max 1.423716 1.302028 1.544809 1.317285 1.418395 1.639772 1.834946 2.660819 1.392086 1.282667 1.299695 1.277193 1.269242 1.310014 1.584219 NaN 1.134053 1.136601 1.100520 1.087872 1.133987 1.229645 1.546495 1.258766 1.215248 1.580827 1.678640 1.627221 1.574349 1.324488 1.464246 1.193524 1.163234 1.181576 1.222366 1.175926 1.237520 1.476765 1.379264 1.716022 1.280244 1.290533 1.243708 1.267371 1.435168 1.342671 1.240983 1.373498 1.607390 2.703056

Orientation optimization, fixed-tilt

Orientation

In [31]:
########## Starting absolute values for track CF-opt
### Data-indexed parameters
data = [
    'OptCF_Azimuth(da)(mustrun)',
    'OptRev_Azimuth(da)(mustrun)',
    'OptRev_Azimuth(rt)(mustrun)',
    'OptRev_Azimuth(da)(curtail)',
    'OptRev_Azimuth(rt)(curtail)',
    'OptCF_Tilt(da)(mustrun)',
    'OptRev_Tilt(da)(mustrun)',
    'OptRev_Tilt(rt)(mustrun)',
    'OptRev_Tilt(da)(curtail)',
    'OptRev_Tilt(rt)(curtail)',
]
colindex = [0, 1, 1, 2, 2, 3, 4, 4, 5, 5,]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right',
             'right','left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],
         mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 
           0.7, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [[2011,2017],slice(None),slice(None),slice(None),slice(None),
            [2011,2017],slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [166, 255],
    [166, 255],
    [166, 255],
    [15, 57],
    [15, 57],
    [15, 57],
]
xlim = [
    [2017, 2018.9],
    [2009.4, 2018],
    [2009.4, 2018],
    [2017, 2018.9],
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [30, 30, 30, 15, 15, 15, ]
minlocs = [2, 2, 2, 3, 3, 3, ]

ylabel = [
    'Azimuth',
    'Azimuth',
    'Azimuth',
    'Tilt',
    'Tilt',
    'Tilt',
]

note = [
    'CF Opt.',
    'Revenue Opt. (must-run)',
    'Revenue Opt. (curtailable)',
    'CF Opt.',
    'Revenue Opt. (must-run)',
    'Revenue Opt. (curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [0.2, 2, 2, 0.2, 2, 2,], 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,1)].legend(
    handles=patches, loc='upper left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Optimized orientation, fixed-tilt', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
In [32]:
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
OptCF_Azimuth(da)(mustrun) OptRev_Azimuth(da)(mustrun) OptRev_Azimuth(rt)(mustrun) OptRev_Azimuth(da)(curtail) OptRev_Azimuth(rt)(curtail) OptCF_Tilt(da)(mustrun) OptRev_Tilt(da)(mustrun) OptRev_Tilt(rt)(mustrun) OptRev_Tilt(da)(curtail) OptRev_Tilt(rt)(curtail)
count 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000
mean 184.613530 226.312989 235.799180 225.792479 232.223856 31.610307 38.497415 43.217181 38.182560 41.462049
std 6.331337 4.734833 4.071666 4.426137 3.020238 1.174554 2.353140 2.082641 2.079906 1.433022
min 168.368400 208.259400 217.818500 208.126500 220.084200 28.692800 33.719100 36.953700 33.660700 36.362300
2.5% 174.967700 216.834040 228.288700 216.572060 224.933360 29.244100 34.734680 39.646380 34.612200 38.203560
25% 179.521200 223.327900 233.398800 222.908900 230.699200 30.914800 36.984000 42.116600 36.800200 40.743900
50% 182.658700 225.990800 235.445400 225.556900 232.543000 31.662200 38.299300 42.938400 38.111600 41.635800
75% 189.929100 229.231100 238.293600 228.674800 234.236400 32.408300 39.873100 44.184700 39.579600 42.374400
97.5% 196.117200 236.494540 244.089680 234.468600 237.494700 33.732000 43.654320 47.713800 42.293100 43.948660
max 200.324200 252.511100 258.640800 238.814700 240.760500 35.208800 53.609600 57.927100 46.326700 45.690200
median
OptCF_Azimuth(da)(mustrun) OptRev_Azimuth(da)(mustrun) OptRev_Azimuth(rt)(mustrun) OptRev_Azimuth(da)(curtail) OptRev_Azimuth(rt)(curtail) OptCF_Tilt(da)(mustrun) OptRev_Tilt(da)(mustrun) OptRev_Tilt(rt)(mustrun) OptRev_Tilt(da)(curtail) OptRev_Tilt(rt)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 182.85950 NaN NaN 180.02020 179.97350 178.9469 NaN 189.09155 NaN NaN 185.20700 186.38850 185.3944 NaN 191.8876 NaN NaN 183.34080 186.80150 183.57140 NaN 189.08495 NaN NaN 185.20700 186.38850 185.39865 NaN 191.4729 NaN NaN 183.13830 186.97695 183.59200 NaN 31.68270 NaN NaN 33.9581 35.40270 34.2143 NaN 32.50395 NaN NaN 30.89860 34.18990 31.58445 NaN 32.99645 NaN NaN 30.88910 33.38310 30.96415 NaN 32.50375 NaN NaN 30.89860 34.18990 31.5899 NaN 32.8645 NaN NaN 30.94230 33.37485 30.98985 NaN
2011 182.85950 183.48340 177.10000 179.93855 179.94565 178.9633 NaN 190.22680 222.96080 179.4847 184.63325 186.09170 185.2421 NaN 193.4248 217.39130 178.30520 183.51755 187.17415 185.31665 NaN 190.22520 222.96270 179.48470 184.59320 186.09170 185.24210 NaN 192.4291 217.17530 178.3052 183.54135 187.07210 185.36140 NaN 31.68200 28.8997 36.69660 33.9147 35.36700 34.2117 NaN 30.60630 27.2647 35.9748 30.65235 33.76565 30.47460 NaN 32.15330 26.35930 35.6608 30.89440 33.39995 29.60360 NaN 30.60510 27.25910 35.9748 30.65195 33.76565 30.4746 NaN 31.8715 26.3096 35.6182 30.90785 33.38760 29.59415 NaN
2012 182.85950 183.49160 177.10000 179.93670 179.91780 179.0359 NaN 196.83100 210.42100 182.1204 187.71980 186.86535 185.5009 NaN 198.1356 204.87860 182.16800 184.37420 188.62590 185.24380 NaN 196.79485 210.47380 182.12040 187.57250 186.86535 185.50090 NaN 197.5551 204.66100 182.1680 184.41290 188.58420 185.24680 NaN 31.67945 28.8915 36.68080 33.9198 35.38485 33.9043 NaN 32.82990 27.8994 36.8646 31.16140 34.37970 31.92840 NaN 32.23590 28.91650 36.0648 30.99330 33.38650 31.43370 NaN 32.82950 27.89850 36.8646 31.16140 34.37970 31.9284 NaN 32.1445 28.9276 36.0648 31.01940 33.43275 31.44710 NaN
2013 182.85950 183.48340 177.16770 179.93215 179.91780 179.0237 NaN 191.41150 199.81260 179.0987 181.78220 183.99810 184.0627 NaN 193.4898 199.14450 178.59320 179.33340 187.91875 181.24930 NaN 191.41150 199.81320 179.09870 181.75645 183.99810 184.06370 NaN 192.8638 199.12200 178.5932 179.22590 187.97260 181.24890 NaN 31.67680 28.8997 36.64780 33.9096 35.36700 33.9444 NaN 30.84760 26.7706 41.5016 32.06795 36.66745 31.65360 NaN 31.82680 27.77040 40.5596 31.69430 35.39440 31.30980 NaN 30.85450 26.77720 41.5016 32.06795 36.66745 31.6555 NaN 31.6921 27.7723 40.5596 31.96320 35.56490 31.31425 NaN
2014 182.70765 183.48340 177.10920 179.51770 179.94565 179.0239 NaN 196.28360 196.34470 177.7055 182.22825 180.56500 178.7690 NaN 202.8121 191.54400 178.20565 180.79725 180.78495 178.14510 NaN 196.28735 196.34690 177.70550 182.16375 180.56500 178.77125 NaN 201.2243 191.53530 178.1628 180.58315 180.74145 178.14530 NaN 31.67520 28.9181 36.64360 31.9505 35.36700 33.9415 NaN 31.28520 27.6948 44.0570 31.82460 41.17560 35.65420 NaN 31.59830 28.06940 44.3025 31.37280 41.47910 35.12470 NaN 31.28245 27.69750 44.0570 31.82145 41.17560 35.6542 NaN 31.4045 28.0687 44.3279 31.37105 41.48505 35.15010 NaN
2015 182.65870 183.47325 177.11885 179.51770 179.91780 179.0239 176.6678 201.51140 209.39860 179.8746 186.17940 183.08700 183.3174 189.10480 211.9209 194.44200 184.20400 184.39200 187.98720 182.20400 184.5094 201.50190 209.39860 179.87435 186.22240 183.08700 183.31740 189.1048 209.1826 194.49590 184.1738 184.52025 187.85990 182.15050 184.5436 31.67520 28.8913 36.73465 31.9505 35.36700 33.9766 33.8381 30.75835 27.0415 41.9972 30.71250 37.85290 33.28960 31.7660 32.56090 26.57110 41.6799 30.66025 37.92910 32.89930 31.8318 30.75740 27.04150 41.9970 30.70985 37.85290 33.2934 31.7660 31.8224 26.5674 41.6737 30.66450 37.92370 32.88600 32.0401
2016 182.65870 183.48060 177.11780 179.31965 179.94565 179.0576 175.6269 207.35340 201.53365 182.6667 188.13190 186.87060 185.8379 197.75705 217.7250 200.52335 188.29590 186.10000 190.67270 185.04970 209.5976 207.22050 201.52835 182.66670 188.06720 186.87060 185.83790 197.7554 215.5064 200.57165 187.7450 186.35185 190.61370 184.97440 204.9632 31.67520 28.8913 36.79230 31.7825 35.36700 33.9415 33.6213 33.26390 28.0822 38.0253 30.71450 35.08710 32.63190 33.7132 37.40870 28.25435 37.7452 30.58480 35.41170 32.40440 36.7933 33.25390 28.08775 38.0253 30.72355 35.08710 32.6339 33.7013 36.6505 28.2964 37.7410 30.75770 34.79315 32.42080 35.9148
2017 182.65870 183.47780 177.08580 179.33225 179.94565 179.0587 178.4129 225.99080 199.45370 181.0400 188.64090 185.01690 184.5971 217.38150 235.4454 199.09790 182.82960 187.34370 185.71100 185.55055 235.9535 225.55690 199.45370 181.03975 188.65640 185.01690 184.59855 216.9094 232.5430 199.04910 182.8080 187.28850 185.66780 185.39975 227.8725 31.66220 28.8911 36.78320 31.8333 35.36700 33.9866 33.2364 38.29930 27.2439 40.5822 30.40280 36.21495 33.36990 36.7391 42.93840 27.17330 41.5447 30.42520 37.23240 33.30000 43.1225 38.11160 27.27030 40.5822 30.40280 36.21495 33.3705 36.5472 41.6358 27.2233 41.4991 30.45320 37.11685 33.29085 38.7322
max
OptCF_Azimuth(da)(mustrun) OptRev_Azimuth(da)(mustrun) OptRev_Azimuth(rt)(mustrun) OptRev_Azimuth(da)(curtail) OptRev_Azimuth(rt)(curtail) OptCF_Tilt(da)(mustrun) OptRev_Tilt(da)(mustrun) OptRev_Tilt(rt)(mustrun) OptRev_Tilt(da)(curtail) OptRev_Tilt(rt)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 200.3242 NaN NaN 186.815 185.7652 187.0084 NaN 203.0964 NaN NaN 194.0741 193.3475 195.8039 NaN 205.0437 NaN NaN 189.2190 196.8200 192.3163 NaN 203.0965 NaN NaN 194.0741 193.3475 195.8039 NaN 204.8800 NaN NaN 189.2183 196.7783 192.3165 NaN 35.2088 NaN NaN 39.6242 36.6046 36.8202 NaN 35.4261 NaN NaN 38.5893 36.6930 35.5558 NaN 52.6320 NaN NaN 37.5111 35.1908 36.2611 NaN 35.4244 NaN NaN 38.5835 36.6930 35.5558 NaN 42.5957 NaN NaN 37.4994 35.2366 36.3277 NaN
2011 200.3242 193.924 182.2855 186.815 185.7652 187.0084 NaN 205.2315 237.6268 185.3909 193.2689 194.5615 193.5793 NaN 207.1296 230.4955 184.0411 191.2672 194.9198 195.1934 NaN 205.2284 237.6268 185.3909 193.2136 194.5615 193.5793 NaN 206.6391 230.2195 184.0411 190.7246 194.8559 195.1934 NaN 35.2088 31.572 39.6473 39.6242 36.6046 36.8202 NaN 37.5061 34.5644 39.7165 37.9905 35.3708 33.7044 NaN 38.6189 29.9489 39.3725 37.3453 35.2511 32.7081 NaN 37.3298 33.1280 39.7165 37.9904 35.3708 33.7044 NaN 36.6292 29.9409 39.3725 37.4805 35.1135 32.5442 NaN
2012 200.3242 193.924 182.2855 186.815 185.7652 187.0084 NaN 214.7674 250.4336 187.4748 195.2608 202.4769 195.1589 NaN 240.5607 243.1014 192.0146 195.9897 200.4531 195.9943 NaN 214.7674 250.4336 187.4748 195.2608 202.4769 195.1589 NaN 240.5353 243.0977 192.0146 190.8303 200.4265 195.6853 NaN 35.2088 31.572 39.6473 39.6242 36.6046 36.8202 NaN 37.6982 33.4546 40.3640 37.5766 37.2344 35.3047 NaN 41.4229 39.0149 39.3825 36.7474 35.4960 35.8552 NaN 37.5482 33.4858 40.3640 37.5766 37.2344 35.3047 NaN 40.8937 39.0145 39.3822 36.6366 35.4857 35.8552 NaN
2013 200.3242 193.924 183.5227 186.815 185.7652 187.0084 NaN 206.9637 227.3251 184.7187 189.2500 193.9252 196.1463 NaN 208.7176 227.1377 185.4995 188.7444 199.6329 207.9853 NaN 206.9637 227.3251 184.7187 189.0939 193.9252 196.1463 NaN 207.9256 227.1282 185.4995 188.5506 199.6361 207.9853 NaN 35.2088 31.572 39.6473 39.6242 36.6046 36.8202 NaN 35.0620 29.7353 45.2676 40.0781 39.8188 36.4184 NaN 35.9119 31.4930 44.7731 40.6843 38.8753 37.1026 NaN 35.0610 29.7353 45.2676 40.0781 39.8188 36.4184 NaN 35.6612 31.5218 44.7769 40.5370 38.8533 37.1026 NaN
2014 200.3242 193.924 183.5227 186.815 185.7652 187.0084 NaN 214.3079 209.5750 182.2823 205.7640 187.6079 188.4843 NaN 224.9102 211.7599 182.3004 201.3445 190.5220 204.2635 NaN 214.3078 209.5750 182.2823 205.7177 187.6079 188.4843 NaN 224.1930 211.7739 182.2620 201.1911 190.4667 204.2635 NaN 35.2088 31.572 39.6473 39.6242 36.6046 36.8202 NaN 34.7048 32.7132 47.1449 40.3402 43.2937 43.6029 NaN 35.3231 35.0470 46.8744 39.9205 44.6806 43.7675 NaN 34.7048 32.7243 47.1449 40.3402 43.2937 43.6029 NaN 34.6671 35.0438 46.9077 39.6111 44.6586 43.6766 NaN
2015 200.3242 193.924 183.5227 186.815 185.7652 187.0084 195.0602 236.0143 221.9458 191.6743 197.2449 193.4627 194.0264 212.0025 239.3812 238.9267 189.9292 202.3802 201.2787 202.2951 207.0136 236.0143 221.9458 191.6743 197.2449 193.4627 194.0264 212.0025 238.7308 238.9868 190.0004 202.3568 201.2246 202.0031 205.8385 35.2088 31.572 39.6473 39.6242 36.6046 36.8202 38.3654 35.3977 30.9044 44.1511 39.2335 40.7012 39.3176 36.0870 39.0796 35.3221 44.3236 39.2039 40.7004 41.1574 37.1280 34.9652 30.9075 44.1511 39.2335 40.7012 39.3176 36.0870 36.4279 35.2816 44.3412 39.2168 40.6639 41.1215 37.0679
2016 200.3242 193.924 183.5227 186.815 185.7652 187.0084 195.0602 219.2098 221.9569 189.9027 197.0652 200.1599 196.2292 217.6408 239.5015 226.9745 198.7042 196.3130 209.5217 195.1880 229.0691 218.9425 220.9647 189.9027 197.0652 200.1599 196.2292 217.6407 229.3088 226.6087 196.5223 196.2718 208.8442 195.1857 226.6412 35.2088 31.572 39.6473 39.6242 36.6046 36.8202 38.3654 36.7116 32.8025 40.6591 39.0716 37.2069 37.7067 38.0778 42.9403 34.4899 40.1544 39.0292 38.5277 38.8492 41.7094 36.7116 32.8025 40.6591 39.0679 37.2069 37.6136 38.0778 41.4139 34.3587 40.1620 38.8385 38.2816 38.3669 40.7555
2017 200.3242 193.924 183.5227 186.815 185.7652 187.0084 196.7155 252.5111 215.9665 189.9117 198.9783 192.5879 195.7794 234.1381 258.6408 216.2665 192.2369 196.8801 200.1029 205.3014 268.0486 238.8147 215.9666 189.9117 198.9783 192.5879 195.7794 233.9650 240.7605 216.1467 192.2021 196.8801 200.0905 205.0568 238.2724 35.2088 31.572 39.6473 39.6242 36.6046 36.8202 38.3654 53.6096 34.5111 44.4107 38.8008 38.7876 38.3568 45.0457 57.9271 41.2006 45.4576 39.8040 41.1456 39.8834 48.7947 46.3267 33.8992 44.2167 38.8237 38.7876 38.3568 44.9395 45.6902 36.3601 44.7203 38.9162 39.9085 39.7439 44.5515
OptCF_Azimuth(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 184.784422 184.780772 184.743928 184.668910 184.625658 184.621536 184.618062 184.613530 183.728347 183.733619 183.729803 183.726376 183.716132 183.716984 183.717720 NaN 177.280703 177.283441 177.355053 177.322469 177.327681 177.314551 177.269676 180.133160 180.177276 180.180880 180.175165 179.569879 179.580764 179.508782 179.534263 179.494221 179.507948 179.523979 179.544076 179.552625 179.549622 179.557790 179.557790 178.987051 178.997281 179.108577 179.101311 179.104540 179.099351 179.115320 179.120117 177.364601 176.287991 178.452795
std 6.329045 6.331391 6.321225 6.318535 6.319824 6.318641 6.319364 6.331337 2.449782 2.450419 2.450925 2.453753 2.459618 2.459784 2.464699 NaN 1.657133 1.630715 1.637373 1.640075 1.604641 1.586874 1.557755 2.291087 2.295743 2.305142 2.289264 2.720174 2.727798 2.759486 2.724459 2.164374 2.174284 2.147454 2.153474 2.151902 2.150333 2.154622 2.154622 1.765614 1.768602 1.883145 1.889437 1.878194 1.875746 1.884853 1.882209 5.421593 4.388795 5.083917
min 168.368400 168.368400 168.368400 168.368400 168.368400 168.368400 168.368400 168.368400 175.637800 175.637800 175.637800 175.637800 175.637800 175.637800 175.637800 NaN 173.379800 173.379800 173.379800 173.379800 173.379800 173.379800 173.379800 175.252100 175.252100 175.252100 175.252100 172.457000 172.457000 171.422300 171.422300 172.556200 172.556200 172.556200 172.556200 172.556200 172.556200 172.556200 172.556200 173.858100 173.858100 173.858100 173.858100 173.858100 173.858100 173.858100 173.858100 164.815200 164.815200 164.815200
2.5% 174.979003 174.981400 174.981400 174.968385 174.967700 174.967700 174.967700 174.967700 178.803600 178.812055 178.811940 178.814720 178.803600 178.803600 178.803600 NaN 174.127100 174.248910 174.348760 174.370025 174.316275 174.319620 174.399250 175.593985 175.633408 175.648570 175.657668 174.290595 174.302365 174.249400 174.249400 174.570705 174.584755 174.601615 174.610045 174.615665 174.617070 174.618475 174.618475 175.777400 175.750450 175.746000 175.746000 175.746000 175.746000 175.746000 175.746075 168.193500 169.035700 170.089750
25% 179.610800 179.610800 179.596650 179.580900 179.568300 179.562725 179.546000 179.521200 182.349300 182.350400 182.350400 182.348200 182.336100 182.336100 182.328750 NaN 176.178900 176.214200 176.280900 176.234525 176.285725 176.280900 176.234375 178.384600 178.411800 178.411800 178.411800 177.930250 177.938750 177.897025 177.906700 178.083500 178.083500 178.083500 178.083500 178.083500 178.083500 178.083500 178.083500 177.778300 177.778300 177.816400 177.792900 177.817500 177.817500 177.817500 177.820250 174.252600 174.216050 175.198400
50% 182.859500 182.859500 182.859500 182.859500 182.707650 182.658700 182.658700 182.658700 183.483400 183.491600 183.483400 183.483400 183.473250 183.480600 183.477800 NaN 177.100000 177.100000 177.167700 177.109200 177.118850 177.117800 177.085800 180.020200 179.938550 179.936700 179.932150 179.517700 179.517700 179.319650 179.332250 179.973500 179.945650 179.917800 179.917800 179.945650 179.917800 179.945650 179.945650 178.946900 178.963300 179.035900 179.023700 179.023900 179.023900 179.057600 179.058700 176.667800 175.626900 178.412900
75% 190.023300 190.023300 189.937800 189.929100 189.882350 189.843050 189.823400 189.929100 185.022500 185.027800 185.027800 185.022500 185.022500 185.022500 185.034250 NaN 178.322700 178.322700 178.350600 178.341450 178.350600 178.350600 178.322700 181.437250 181.614875 181.592400 181.501725 181.338800 181.338800 181.338800 181.278475 180.576500 180.576500 180.576500 180.576500 180.576500 180.576500 180.576500 180.576500 180.073600 180.094400 180.208600 180.204800 180.204800 180.186300 180.220000 180.213925 178.677300 177.363700 180.064000
97.5% 196.211000 196.211000 196.211000 196.206310 196.133615 196.128925 196.126580 196.117200 189.176300 189.171445 189.170735 189.170380 189.170202 189.170202 189.171445 NaN 181.359310 181.184100 181.077700 181.016900 180.977275 180.917730 180.480200 185.031300 185.031300 185.041230 185.033782 184.948165 184.945755 184.926475 184.908400 184.351472 184.322197 184.287068 184.295965 184.287905 184.285890 184.283875 184.283875 182.651575 182.582850 183.351800 183.351800 183.345425 183.342300 183.351800 183.351800 190.868700 189.587600 190.868700
max 200.324200 200.324200 200.324200 200.324200 200.324200 200.324200 200.324200 200.324200 193.924000 193.924000 193.924000 193.924000 193.924000 193.924000 193.924000 NaN 182.285500 182.285500 183.522700 183.522700 183.522700 183.522700 183.522700 186.815000 186.815000 186.815000 186.815000 186.815000 186.815000 186.815000 186.815000 185.765200 185.765200 185.765200 185.765200 185.765200 185.765200 185.765200 185.765200 187.008400 187.008400 187.008400 187.008400 187.008400 187.008400 187.008400 187.008400 195.060200 195.060200 196.715500
OptRev_Azimuth(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.00000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 190.172515 191.435390 197.133760 192.353465 196.765930 201.921176 207.431811 226.312989 222.554475 210.921695 200.569615 196.711416 209.389675 201.817638 199.465265 NaN 179.864122 182.207962 179.255217 177.765261 180.021849 182.86952 181.343592 185.330886 184.672530 187.607686 182.006323 183.526219 186.222462 188.310853 188.779419 185.571790 185.499930 188.097716 184.466650 180.420395 183.476743 188.001723 185.197625 185.388161 185.336810 185.564738 184.234134 178.981831 183.591682 186.043262 184.750200 189.593855 197.073607 217.224124
std 4.733824 5.296078 5.460843 5.080868 5.749565 7.318307 5.788595 4.734833 3.751428 5.677455 3.908703 3.668117 3.258881 3.113769 4.160837 NaN 1.889165 1.952703 1.416455 1.441744 1.966422 1.93331 1.808426 2.270712 2.269169 2.416951 2.399258 5.712800 3.634844 2.467944 3.258372 3.346139 3.724804 4.438360 3.468057 2.722997 2.637872 3.849073 2.523436 2.026393 2.197698 1.984219 2.113156 2.209310 2.268179 2.302082 2.216620 6.775977 6.903429 7.897510
min 175.340900 176.405300 180.605700 173.960700 171.891400 182.139600 190.686300 208.259400 192.539900 119.510100 190.768900 179.893800 197.914300 192.973700 177.030800 NaN 175.968400 177.570100 175.728300 173.078000 175.108100 178.48690 177.319000 178.818400 178.562400 181.727500 176.360900 173.142700 176.491600 180.670200 180.997000 176.937500 176.476700 180.672700 175.970400 171.823000 175.978600 180.183400 177.912700 178.997400 178.424000 178.882300 177.815000 172.215900 176.995500 179.949900 178.741100 168.892800 169.045900 170.189200
2.5% 183.127505 183.450000 187.849843 183.998045 187.632712 191.087187 196.137100 216.834040 214.761765 201.199075 194.341000 189.987220 203.361928 196.454435 191.912025 NaN 176.813910 178.757700 177.031560 175.326797 177.342700 179.29067 178.345200 180.510080 180.378213 182.343500 177.834052 176.617545 179.618607 183.808275 183.941798 178.320975 178.224330 181.729798 178.147600 173.913990 178.491765 182.171400 179.341913 181.367000 181.314300 181.938040 180.551900 175.132375 179.707640 182.005650 180.728338 178.491380 181.781422 194.593000
25% 186.435775 187.331400 192.949150 188.231750 191.762475 196.647400 202.757100 223.327900 220.762050 208.724000 197.909450 194.192100 207.082925 199.929350 196.624650 NaN 178.733400 180.832000 178.270300 176.703175 178.819250 181.54220 180.203925 183.832300 183.357400 186.409900 180.346650 179.684950 183.615700 186.464350 186.107700 183.439925 182.773200 185.665700 182.471525 178.850425 182.367900 186.041425 184.111000 184.045900 183.893350 184.255650 182.790275 177.488975 182.045700 184.423300 183.326325 185.874400 194.587025 214.625600
50% 189.091550 190.226800 196.831000 191.411500 196.283600 201.511400 207.353400 225.990800 222.960800 210.421000 199.812600 196.344700 209.398600 201.533650 199.453700 NaN 179.484700 182.120400 179.098700 177.705500 179.874600 182.66670 181.040000 185.207000 184.633250 187.719800 181.782200 182.228250 186.179400 188.131900 188.640900 186.388500 186.091700 186.865350 183.998100 180.565000 183.087000 186.870600 185.016900 185.394400 185.242100 185.500900 184.062700 178.769000 183.317400 185.837900 184.597100 189.104800 197.757050 217.381500
75% 193.507125 195.459200 200.599075 196.207200 201.428675 206.602950 212.047200 229.231100 224.934050 213.054850 202.947950 199.220400 211.806100 203.746700 202.335100 NaN 180.847500 183.353950 179.997400 178.640525 180.758400 184.00490 182.318200 186.598750 185.568800 188.743400 183.499700 185.616600 188.735500 189.675775 190.856350 187.730400 186.814500 188.309100 185.115575 181.883600 185.073400 188.814825 186.673950 186.677225 186.736175 186.781350 185.488850 180.324075 184.905300 187.517000 185.885700 191.810400 199.863100 220.414100
97.5% 199.657700 201.808600 208.262428 202.387590 207.194850 216.846275 217.065800 236.494540 228.438245 221.701710 208.028230 203.873100 215.098478 208.162045 206.626935 NaN 184.819380 186.430540 182.485900 180.880410 183.641800 187.27300 186.047500 190.246720 190.088628 192.961040 187.768100 201.029195 193.435400 193.506075 195.945378 191.205827 192.851587 198.140497 191.815540 185.757200 188.576460 197.989413 190.095413 189.503398 190.007938 190.133555 188.965600 183.873000 188.852260 191.069500 190.000837 206.197340 212.630538 231.369250
max 203.096400 205.231500 214.767400 206.963700 214.307900 236.014300 219.209800 252.511100 237.626800 250.433600 227.325100 209.575000 221.945800 221.956900 215.966500 NaN 185.390900 187.474800 184.718700 182.282300 191.674300 189.90270 189.911700 194.074100 193.268900 195.260800 189.250000 205.764000 197.244900 197.065200 198.978300 193.347500 194.561500 202.476900 193.925200 187.607900 193.462700 200.159900 192.587900 195.803900 193.579300 195.158900 196.146300 188.484300 194.026400 196.229200 195.779400 212.002500 217.640800 234.138100
OptRev_Azimuth(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 192.863685 194.056246 200.428547 194.294435 203.275124 211.631095 218.677032 235.799180 217.333955 205.696552 199.593010 191.666016 194.832649 200.717598 199.032793 NaN 178.604776 182.163869 178.761399 178.252055 184.248032 187.989698 182.991861 183.187047 183.538641 184.328933 179.941275 181.437930 184.670105 186.247951 187.594708 186.751352 186.833574 188.707529 188.302579 180.712431 188.172034 192.701954 186.922429 183.622990 185.450960 185.392699 182.266469 178.487500 182.594101 185.266991 185.806670 184.882650 209.014944 230.696599
std 4.590793 4.802732 11.877469 6.059847 6.269247 6.978609 6.483776 4.071666 5.172406 5.766333 5.285122 3.821839 4.098195 4.096775 5.756059 NaN 1.972163 2.126328 1.778669 1.464278 2.013315 2.781292 2.320403 2.346776 2.572994 2.771527 2.782634 5.214247 5.408519 3.548423 3.348196 3.729056 3.381006 4.338021 3.401446 3.664179 3.682295 5.711433 4.043769 2.355872 3.043629 2.170106 5.174045 3.098406 2.735840 2.573385 2.498767 4.916169 10.748611 15.592437
min 177.838700 174.238700 160.269700 175.642200 174.474400 183.909000 197.535800 217.818500 186.845700 189.777000 176.206200 172.358300 179.346200 184.346300 158.955300 NaN 174.413500 177.281300 174.167300 173.683900 179.170600 178.335900 175.220800 176.843900 177.626100 178.388100 174.102900 170.763300 172.170000 173.609300 177.487100 176.844400 179.660000 178.270600 180.112900 168.589600 178.805500 180.667500 177.894700 172.272000 175.050100 178.276900 171.229800 170.269900 174.195800 171.486800 173.370500 168.167600 172.983400 179.404300
2.5% 185.976873 184.407000 173.023383 184.825530 193.033900 199.083225 207.528640 228.288700 206.080935 197.907700 188.991965 184.720100 187.992218 193.554688 186.773965 NaN 175.009500 178.253525 175.779600 175.615000 180.643500 182.363550 178.668138 178.667235 179.134165 179.760290 175.489352 174.135993 175.619878 179.701000 182.193308 178.688287 181.399950 180.635148 181.849250 173.245780 181.659130 184.065700 180.473625 179.345500 179.695437 181.670290 177.063600 173.498975 178.290740 180.736200 181.676325 175.900720 186.512500 198.372700
25% 189.324400 191.075300 194.474775 189.635700 198.133750 206.580875 213.594200 233.398800 214.112500 202.755100 196.772650 189.416900 192.223625 198.738400 195.745500 NaN 177.290300 180.749350 177.578300 177.281350 182.910625 186.242600 181.359375 181.614700 181.936850 182.453400 178.054275 177.389350 180.117800 184.001625 185.169825 184.218725 184.205150 186.100225 186.517975 178.907350 185.663900 189.773925 185.209500 182.007950 183.431200 183.960050 179.530325 176.275575 180.786400 183.591900 184.164100 181.758100 201.823400 217.037200
50% 191.887600 193.424800 198.135600 193.489800 202.812100 211.920900 217.725000 235.445400 217.391300 204.878600 199.144500 191.544000 194.442000 200.523350 199.097900 NaN 178.305200 182.168000 178.593200 178.205650 184.204000 188.295900 182.829600 183.340800 183.517550 184.374200 179.333400 180.797250 184.392000 186.100000 187.343700 186.801500 187.174150 188.625900 187.918750 180.784950 187.987200 190.672700 185.711000 183.571400 185.316650 185.243800 181.249300 178.145100 182.204000 185.049700 185.550550 184.509400 209.597600 235.953500
75% 196.432475 197.159400 206.819150 198.347500 207.884600 216.633000 224.409900 238.293600 221.167850 207.380000 202.497650 193.946400 197.088575 202.273125 202.939550 NaN 179.702900 183.412250 179.495100 179.062600 185.562575 189.858700 184.534850 184.662950 185.090450 186.138800 181.788600 183.824450 188.900775 188.407225 190.182125 189.450300 189.226900 190.765950 189.879125 181.895675 190.638600 195.141175 188.411700 185.126375 187.461350 186.596150 183.073900 180.227850 183.922400 186.770000 187.172000 186.879800 217.616600 244.054100
97.5% 201.661900 202.701900 231.479788 206.104195 215.343800 222.698625 230.520460 244.089680 226.163450 219.122300 210.307100 199.498680 203.609355 211.971107 208.459400 NaN 183.127020 186.729470 183.166100 181.433030 188.549750 192.381100 187.517025 187.897475 189.241122 189.246370 185.792777 195.674560 195.832800 193.386050 193.949280 192.506362 192.794670 197.640595 195.157545 189.779600 195.090960 205.927488 196.266938 188.538483 191.418325 190.397235 201.984610 185.373075 189.380000 191.239650 191.669625 196.713160 223.675880 250.556600
max 205.043700 207.129600 240.560700 208.717600 224.910200 239.381200 239.501500 258.640800 230.495500 243.101400 227.137700 211.759900 238.926700 226.974500 216.266500 NaN 184.041100 192.014600 185.499500 182.300400 189.929200 198.704200 192.236900 189.219000 191.267200 195.989700 188.744400 201.344500 202.380200 196.313000 196.880100 196.820000 194.919800 200.453100 199.632900 190.522000 201.278700 209.521700 200.102900 192.316300 195.193400 195.994300 207.985300 204.263500 202.295100 195.188000 205.301400 207.013600 229.069100 268.048600
OptRev_Azimuth(da)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 190.170495 191.459930 197.106450 192.352177 196.769575 201.893079 207.359627 225.792479 222.559863 210.939877 200.568184 196.713805 209.391117 201.807599 199.469483 NaN 179.864121 182.208520 179.255464 177.765300 180.021670 182.869323 181.342236 185.323825 184.670352 187.613206 181.988803 183.528810 186.238300 188.304485 188.795199 185.571888 185.499930 188.098264 184.466369 180.420395 183.477390 188.002034 185.197515 185.391518 185.339007 185.566772 184.234280 178.983590 183.587144 186.044594 184.751667 189.593781 197.066711 216.784930
std 4.734683 5.270091 5.437742 5.079368 5.743449 7.296453 5.733881 4.426137 3.755298 5.498849 3.908129 3.666889 3.256895 3.077095 4.130284 NaN 1.889166 1.952892 1.416390 1.441690 1.966365 1.933056 1.805898 2.274120 2.268193 2.374925 2.390748 5.716153 3.604148 2.468537 3.251710 3.345958 3.724804 4.437623 3.467201 2.722997 2.637987 3.848915 2.523593 2.024773 2.193047 1.980953 2.112494 2.207787 2.261124 2.302735 2.215346 6.775994 6.896367 7.843399
min 175.337700 176.375100 180.592100 173.960700 171.890200 182.139600 190.671200 208.126500 192.530000 138.981700 190.768900 179.893800 197.914300 192.973700 182.854100 NaN 175.968400 177.570100 175.728300 173.078000 175.108100 178.486900 177.319000 178.818400 178.562400 181.727500 176.360900 173.142700 176.491600 180.670200 180.997300 176.937500 176.476700 180.672700 175.970400 171.823000 175.978600 180.183400 177.912700 178.997400 178.424000 178.882300 177.815000 172.215900 176.995500 179.949900 178.741100 168.892800 169.046200 170.337300
2.5% 183.150165 183.544600 187.544403 183.997870 187.631392 191.087975 196.262370 216.572060 214.756390 200.899145 194.341000 189.987220 203.361928 196.609358 191.924510 NaN 176.813910 178.757700 177.031560 175.326797 177.342600 179.290670 178.382125 180.510080 180.378213 182.412390 177.834052 176.587267 179.618607 183.827650 183.941798 178.320975 178.224330 181.729798 178.147600 173.913990 178.491765 182.171400 179.341913 181.367000 181.314300 181.957645 180.551900 175.139900 179.707580 182.005650 180.746000 178.491380 181.781422 194.569850
25% 186.434300 187.353900 192.995400 188.230850 191.763050 196.644000 202.718500 222.908900 220.763050 208.724000 197.909450 194.190600 207.082925 199.953025 196.618150 NaN 178.733400 180.832000 178.270300 176.703500 178.819250 181.542200 180.203925 183.832300 183.357400 186.409900 180.346600 179.657625 183.615700 186.426875 186.111050 183.439925 182.773200 185.665700 182.460900 178.850425 182.367900 186.041425 184.111000 184.055125 183.893350 184.255650 182.790275 177.490575 182.045700 184.423400 183.326325 185.874400 194.596300 214.074000
50% 189.084950 190.225200 196.794850 191.411500 196.287350 201.501900 207.220500 225.556900 222.962700 210.473800 199.813200 196.346900 209.398600 201.528350 199.453700 NaN 179.484700 182.120400 179.098700 177.705500 179.874350 182.666700 181.039750 185.207000 184.593200 187.572500 181.756450 182.163750 186.222400 188.067200 188.656400 186.388500 186.091700 186.865350 183.998100 180.565000 183.087000 186.870600 185.016900 185.398650 185.242100 185.500900 184.063700 178.771250 183.317400 185.837900 184.598550 189.104800 197.755400 216.909400
75% 193.506525 195.456700 200.566625 196.202900 201.427900 206.590025 212.005200 228.674800 224.933700 213.084700 202.947950 199.191400 211.806100 203.739425 202.335100 NaN 180.847500 183.353950 179.997400 178.640525 180.758200 184.004900 182.318200 186.598750 185.568800 188.728500 183.492350 185.648100 188.735500 189.675775 190.943900 187.730400 186.814500 188.309100 185.115575 181.883600 185.073400 188.814825 186.673950 186.679875 186.736175 186.781700 185.488850 180.311300 184.893100 187.517000 185.883200 191.810400 199.843200 219.817300
97.5% 199.657700 201.730500 208.237943 202.387590 207.193985 216.846275 216.898100 234.468600 228.393905 221.701650 208.034605 203.798400 215.098478 208.161978 206.627045 NaN 184.819380 186.430540 182.485900 180.880410 183.641800 187.273000 186.047500 190.246060 190.088538 192.933750 187.699800 201.029195 193.435400 193.506075 195.945378 191.205827 192.851587 198.140497 191.815540 185.757200 188.576460 197.989413 190.095413 189.502995 190.007938 190.133555 188.964790 183.870200 188.851000 191.083300 190.000837 206.197340 212.630538 230.677750
max 203.096500 205.228400 214.767400 206.963700 214.307800 236.014300 218.942500 238.814700 237.626800 250.433600 227.325100 209.575000 221.945800 220.964700 215.966600 NaN 185.390900 187.474800 184.718700 182.282300 191.674300 189.902700 189.911700 194.074100 193.213600 195.260800 189.093900 205.717700 197.244900 197.065200 198.978300 193.347500 194.561500 202.476900 193.925200 187.607900 193.462700 200.159900 192.587900 195.803900 193.579300 195.158900 196.146300 188.484300 194.026400 196.229200 195.779400 212.002500 217.640700 233.965000
OptRev_Azimuth(rt)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 192.419556 193.190410 200.780726 193.775473 201.874735 209.344089 215.746731 232.223856 217.207918 205.600715 199.597918 191.679516 194.891780 200.748629 199.115903 NaN 178.612782 182.162535 178.759938 178.205422 184.284183 187.462607 182.979407 183.126053 183.660206 184.207398 179.850687 181.361563 184.710518 186.259108 187.678018 186.874467 186.739800 188.699230 188.384414 180.646990 188.052197 191.887100 186.445818 183.659708 185.506105 185.398152 182.272638 178.481143 182.539454 185.217255 185.694667 185.061463 204.784867 222.026744
std 4.702467 4.439599 10.535536 5.836348 5.967621 6.092274 5.206692 3.020238 5.270372 5.810476 5.262522 3.802079 4.038446 4.001652 5.397037 NaN 1.968565 2.115878 1.778720 1.461869 2.010019 2.771401 2.269687 2.264569 2.502218 2.512231 2.620156 5.179104 5.314334 3.391551 3.281953 3.611718 3.377352 4.308749 3.338418 3.656080 3.710345 4.879803 3.849713 2.300042 2.998700 2.165532 5.165609 3.078277 2.703694 2.537915 2.458549 4.754723 10.810255 11.226809
min 176.891100 179.317900 179.337300 175.180000 173.988400 186.012900 195.925700 220.084200 189.388500 189.477300 176.793600 172.359300 179.342300 184.775900 161.297400 NaN 174.413500 177.281300 174.167200 173.672800 179.169100 178.258600 177.372300 176.976100 177.971000 178.385400 174.137500 170.762300 174.029100 176.094200 177.487100 178.252700 179.165100 178.329800 180.759800 168.939200 178.528100 180.572500 178.178300 176.179600 175.086300 178.244200 171.233100 170.269100 174.058000 171.410000 173.917300 169.246800 169.520600 178.114600
2.5% 185.378520 185.386300 186.452105 184.839625 192.780600 199.094738 206.428880 224.933360 205.386980 197.779675 189.494650 184.738080 188.322770 193.972232 189.141645 NaN 175.009500 178.405750 175.779600 175.600200 180.679575 181.815670 178.908987 178.632170 179.419418 179.622350 175.975572 173.505115 176.126792 179.933300 182.333650 178.978800 181.102345 180.725900 182.236883 173.105678 181.596475 184.657025 180.531325 179.432530 179.738600 181.719095 177.056502 173.514100 178.279240 180.715850 181.675938 176.926700 184.417100 196.507450
25% 188.727050 190.004400 194.272225 189.162450 196.856425 205.205075 211.879200 230.699200 214.035850 202.663800 196.780150 189.489300 192.369025 198.797950 195.723550 NaN 177.290300 180.742650 177.576800 177.220075 182.966400 185.604800 181.343925 181.595500 181.997025 182.542000 178.057100 177.384000 180.255775 183.937050 185.272200 184.456825 184.073400 186.175625 186.936675 178.836875 185.549150 189.614400 184.823775 182.066375 183.488800 183.960900 179.527600 176.301375 180.707500 183.570000 184.100950 181.927400 196.678225 213.277000
50% 191.472900 192.429100 197.555100 192.863800 201.224300 209.182600 215.506400 232.543000 217.175300 204.661000 199.122000 191.535300 194.495900 200.571650 199.049100 NaN 178.305200 182.168000 178.593200 178.162800 184.173800 187.745000 182.808000 183.138300 183.541350 184.412900 179.225900 180.583150 184.520250 186.351850 187.288500 186.976950 187.072100 188.584200 187.972600 180.741450 187.859900 190.613700 185.667800 183.592000 185.361400 185.246800 181.248900 178.145300 182.150500 184.974400 185.399750 184.543600 204.963200 227.872500
75% 196.120775 196.240100 206.011875 197.658350 206.373325 213.358400 219.259000 234.236400 221.174700 207.423350 202.482550 193.976700 197.030375 202.249475 202.869050 NaN 179.702900 183.400100 179.493700 178.993425 185.599025 189.283500 184.472825 184.377150 185.138250 185.834900 181.604625 183.998650 188.896375 188.404500 190.337850 189.445800 189.077150 190.753500 189.932700 181.915300 190.542900 193.663900 186.765675 185.144475 187.505575 186.609850 183.073900 180.237450 183.852100 186.713700 186.984525 186.999600 214.792300 230.588200
97.5% 201.466358 201.567800 231.673400 205.103000 212.672180 218.906213 225.662500 237.494700 226.154075 219.152500 210.302595 199.438920 203.557652 211.948830 208.273320 NaN 183.127020 186.729470 183.166100 181.371900 188.596113 191.970150 187.485813 187.593350 189.509525 189.097050 185.631000 195.603872 196.050263 192.923800 193.911162 192.496375 192.700745 197.603997 195.171310 189.641100 194.952765 202.558600 195.474750 188.425652 191.404000 190.376400 201.984610 185.319500 189.151120 191.159350 191.445125 197.062740 218.988680 235.007500
max 204.880000 206.639100 240.535300 207.925600 224.193000 238.730800 229.308800 240.760500 230.219500 243.097700 227.128200 211.773900 238.986800 226.608700 216.146700 NaN 184.041100 192.014600 185.499500 182.262000 190.000400 196.522300 192.202100 189.218300 190.724600 190.830300 188.550600 201.191100 202.356800 196.271800 196.880100 196.778300 194.855900 200.426500 199.636100 190.466700 201.224600 208.844200 200.090500 192.316500 195.193400 195.685300 207.985300 204.263500 202.003100 195.185700 205.056800 205.838500 226.641200 238.272400
OptCF_Tilt(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 31.633125 31.636149 31.631682 31.619229 31.614647 31.612739 31.612102 31.610307 28.292342 28.289521 28.292366 28.296933 28.286637 28.284956 28.281295 NaN 36.638339 36.636416 36.639161 36.632402 36.741684 36.770468 36.773347 34.263909 34.263430 34.266644 34.237802 31.512273 31.519112 31.385628 31.431940 34.537130 34.511823 34.535154 34.523170 34.515811 34.512483 34.507014 34.507014 33.858432 33.850499 33.658997 33.677204 33.666880 33.677965 33.660617 33.672899 34.148487 33.906856 33.227356
std 1.176165 1.177897 1.176649 1.173946 1.174922 1.176129 1.176252 1.174554 1.796051 1.800043 1.801218 1.801404 1.804111 1.805076 1.807607 NaN 1.026728 1.013185 1.033411 1.022697 1.012513 1.001989 0.963586 2.013093 2.072248 2.053242 2.055379 3.520039 3.514637 3.502522 3.490558 1.779196 1.774714 1.761480 1.764593 1.764929 1.764260 1.765927 1.765927 1.482136 1.481705 1.558943 1.561142 1.553388 1.550470 1.543199 1.545698 1.310803 1.094678 1.723585
min 28.692800 28.692800 28.692800 28.692800 28.692800 28.692800 28.692800 28.692800 21.678000 21.678000 21.678000 21.678000 21.678000 21.678000 21.678000 NaN 34.046300 34.046300 34.046300 34.046300 34.046300 34.046300 34.046300 29.648300 29.648300 29.648300 29.648300 25.816400 25.816400 25.816400 25.816400 29.549900 29.549900 29.549900 29.549900 29.549900 29.549900 29.549900 29.549900 29.418000 29.418000 28.778100 28.778100 28.778100 28.778100 28.778100 28.778100 30.725900 30.725900 30.233300
2.5% 29.253600 29.253600 29.253600 29.253600 29.247225 29.247475 29.247600 29.244100 24.784270 24.701335 24.707405 24.710440 24.711957 24.711957 24.701335 NaN 34.487800 34.487800 34.487800 34.487800 34.613000 34.625300 34.875800 31.089660 31.294670 31.373520 30.936645 26.092878 26.095842 26.119563 26.107702 30.749800 30.749800 30.749800 30.747573 30.748382 30.748585 30.748788 30.748788 31.288827 31.287112 30.895160 30.893000 30.889700 30.916120 30.891350 30.889700 32.342480 32.383000 30.847000
25% 30.934500 30.943300 30.930150 30.928700 30.926700 30.926250 30.926100 30.914800 26.933950 26.931200 26.928000 26.933900 26.923600 26.898625 26.895400 NaN 35.962300 35.968400 35.968400 35.968400 36.057600 36.142000 36.200050 32.688850 32.582200 32.586600 32.582200 28.173500 28.176950 28.173500 28.173500 33.065025 33.062800 33.136500 33.093300 33.065025 33.065450 33.062800 33.062800 32.434900 32.430000 32.291150 32.297500 32.295150 32.300300 32.295200 32.299625 33.282400 33.217600 31.506200
50% 31.682700 31.682000 31.679450 31.676800 31.675200 31.675200 31.675200 31.662200 28.899700 28.891500 28.899700 28.918100 28.891300 28.891300 28.891100 NaN 36.696600 36.680800 36.647800 36.643600 36.734650 36.792300 36.783200 33.958100 33.914700 33.919800 33.909600 31.950500 31.950500 31.782500 31.833300 35.402700 35.367000 35.384850 35.367000 35.367000 35.367000 35.367000 35.367000 34.214300 34.211700 33.904300 33.944400 33.941500 33.976600 33.941500 33.986600 33.838100 33.621300 33.236400
75% 32.435850 32.436500 32.433900 32.409100 32.408300 32.408300 32.408300 32.408300 29.760100 29.760800 29.768000 29.769800 29.763575 29.763575 29.762650 NaN 37.319800 37.319800 37.322600 37.319800 37.377075 37.414600 37.350925 35.658450 35.743200 35.743200 35.684400 34.119025 34.128875 33.974800 34.062600 35.853400 35.833000 35.829925 35.773750 35.758925 35.749950 35.740975 35.740975 35.100100 35.096475 35.032900 35.049300 35.032200 35.039200 35.021600 35.030350 34.865100 34.379600 34.350000
97.5% 33.732000 33.775800 33.775800 33.732000 33.732000 33.732000 33.732000 33.732000 30.703435 30.716665 30.720035 30.719880 30.719803 30.719803 30.720345 NaN 38.838930 38.827515 38.710400 38.682400 38.639650 38.638720 38.633412 38.634015 38.675297 38.666910 38.661878 38.240617 38.223622 38.087663 37.905757 36.331700 36.331700 36.331700 36.331700 36.331700 36.331700 36.331700 36.331700 36.061900 36.061900 36.048570 36.017100 35.999837 35.999720 35.986100 35.991000 37.138500 36.902842 36.873000
max 35.208800 35.208800 35.208800 35.208800 35.208800 35.208800 35.208800 35.208800 31.572000 31.572000 31.572000 31.572000 31.572000 31.572000 31.572000 NaN 39.647300 39.647300 39.647300 39.647300 39.647300 39.647300 39.647300 39.624200 39.624200 39.624200 39.624200 39.624200 39.624200 39.624200 39.624200 36.604600 36.604600 36.604600 36.604600 36.604600 36.604600 36.604600 36.604600 36.820200 36.820200 36.820200 36.820200 36.820200 36.820200 36.820200 36.820200 38.365400 38.365400 38.365400
OptRev_Tilt(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 32.434073 30.633941 32.835953 30.765654 31.016143 30.673733 33.017629 38.497415 27.158319 27.499473 26.510159 27.108373 26.559197 27.498930 26.800499 NaN 36.095719 36.766789 41.460856 44.048760 41.896108 37.856136 40.551380 31.803550 31.325470 31.795204 32.799691 30.733129 29.889822 30.381382 30.052923 33.230544 32.748598 33.439968 35.475702 39.672397 36.639324 33.802370 35.413205 31.460275 30.223222 31.803913 31.404158 35.811761 32.999382 32.403869 33.423539 32.095429 33.940439 36.982576
std 1.191404 1.370207 1.345048 1.343109 1.537241 1.555587 1.718495 2.353140 1.633360 1.658016 1.734769 2.105055 1.591631 1.960881 1.584643 NaN 1.142663 1.249320 1.413296 1.147406 1.075686 1.176071 1.184998 2.556460 2.426705 2.012710 2.423249 4.756844 3.972121 3.445544 3.818540 2.032493 1.743350 2.062154 2.712845 3.105415 2.663766 2.725364 2.335739 1.400214 1.372527 1.386078 1.503773 2.629449 2.185292 1.382845 1.696028 1.314602 1.215821 3.092808
min 29.341200 27.594800 29.575500 27.449100 25.430300 26.079300 28.415900 33.719100 20.065000 21.519300 20.845500 13.562100 22.031500 19.448500 21.152200 NaN 33.052900 33.693800 38.209100 41.187100 39.217100 34.656000 35.821200 27.084500 26.168800 27.757500 27.290400 20.604400 23.424900 24.164800 23.523100 27.496300 27.894000 28.045800 28.921200 30.589300 30.328200 26.745600 28.045500 27.375500 26.092500 27.923600 27.236000 28.861800 28.602800 28.281800 27.898000 29.292700 30.277800 30.378200
2.5% 30.093837 28.346300 30.395188 28.162050 27.038660 26.903613 29.168180 34.734680 23.792285 24.175300 22.584300 22.701340 23.058705 23.102500 23.555700 NaN 33.900930 34.321300 38.741100 41.808170 39.860700 35.092500 37.707800 28.407700 28.085693 29.228960 29.427295 21.732817 23.679503 25.002775 24.264900 29.516320 28.955715 29.453300 30.282400 32.735140 31.191400 27.824187 29.316525 29.123000 27.775487 29.467600 28.902435 31.879787 29.685500 29.976150 30.678850 30.174900 32.152455 32.779900
25% 31.613900 29.604200 31.901800 30.006300 30.361775 29.813600 31.974400 36.984000 26.145300 26.426750 25.373750 26.085500 25.437975 26.284900 25.727850 NaN 35.459400 35.867400 40.514400 43.275275 41.149550 37.080400 39.894500 30.006150 29.550475 30.243400 31.130000 26.811800 26.424800 27.622925 26.629900 31.495725 31.771225 31.562750 33.078125 37.530325 34.016750 31.240725 33.872150 30.197125 29.029700 30.603200 30.101800 33.632225 30.939600 31.178800 31.987100 31.157400 33.068900 34.325500
50% 32.503950 30.606300 32.829900 30.847600 31.285200 30.758350 33.263900 38.299300 27.264700 27.899400 26.770600 27.694800 27.041500 28.082200 27.243900 NaN 35.974800 36.864600 41.501600 44.057000 41.997200 38.025300 40.582200 30.898600 30.652350 31.161400 32.067950 31.824600 30.712500 30.714500 30.402800 34.189900 33.765650 34.379700 36.667450 41.175600 37.852900 35.087100 36.214950 31.584450 30.474600 31.928400 31.653600 35.654200 33.289600 32.631900 33.369900 31.766000 33.713200 36.739100
75% 33.324400 31.588900 33.752850 31.626300 32.006975 31.690625 34.333700 39.873100 28.510250 28.723800 27.950350 28.525700 27.789200 28.988350 27.983850 NaN 36.688600 37.723250 42.192200 44.824125 42.686100 38.640400 41.354600 33.833500 32.621300 32.944000 34.514750 34.671100 32.811725 32.781550 32.793450 34.882100 34.008425 35.240675 37.800700 41.859700 38.420800 36.044200 36.886550 32.583600 31.273825 32.867900 32.506325 37.498500 34.438300 33.522200 34.652325 32.836700 34.512200 38.585500
97.5% 34.697930 33.749200 35.358770 33.179060 33.334300 33.383212 35.664300 43.654320 29.589710 29.853600 29.037400 30.042540 28.760005 29.986728 29.120540 NaN 38.875460 39.273955 44.616600 46.418548 43.681712 39.901600 42.665363 37.415685 36.849260 36.468360 38.068022 38.391570 36.795325 37.541300 37.025157 35.555665 35.075135 35.718350 39.254500 42.971477 40.297300 36.673800 38.044787 33.946085 32.570137 34.441500 34.360100 41.356437 37.040400 34.705050 36.600800 34.891200 36.878600 43.725600
max 35.426100 37.506100 37.698200 35.062000 34.704800 35.397700 36.711600 53.609600 34.564400 33.454600 29.735300 32.713200 30.904400 32.802500 34.511100 NaN 39.716500 40.364000 45.267600 47.144900 44.151100 40.659100 44.410700 38.589300 37.990500 37.576600 40.078100 40.340200 39.233500 39.071600 38.800800 36.693000 35.370800 37.234400 39.818800 43.293700 40.701200 37.206900 38.787600 35.555800 33.704400 35.304700 36.418400 43.602900 39.317600 37.706700 38.356800 36.087000 38.077800 45.045700
OptRev_Tilt(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 33.013682 32.280974 32.587352 31.747961 31.375414 32.692273 37.283220 43.217181 26.120193 28.354953 27.503510 27.585834 25.907481 27.613885 26.707719 NaN 35.794053 35.997743 40.585449 44.226671 41.592995 37.554075 41.550309 31.509001 31.378751 31.751390 32.689352 30.458299 29.766970 30.186035 30.041975 32.532563 32.495469 32.813139 34.815097 40.012609 36.587045 34.450624 36.392932 30.878538 29.534914 31.445787 31.204277 35.299700 32.658465 32.243852 33.553628 31.959440 36.893790 42.079869
std 1.447432 1.361375 2.100029 1.422111 1.669575 2.480109 1.753088 2.082641 1.584116 2.242693 1.882021 1.962514 1.917089 2.143363 1.873789 NaN 1.186228 1.288861 1.437732 1.041059 1.099977 1.255332 1.227493 2.312640 2.210704 2.005046 2.518508 4.407745 4.114228 3.473793 3.812324 1.925641 1.739007 1.898587 2.336692 3.286824 2.899078 2.829387 2.504529 1.190845 1.226819 1.436687 1.534987 2.787362 2.285731 1.468202 2.016650 1.281428 2.095167 3.401052
min 30.106700 29.164100 27.794800 28.461500 24.964700 26.201300 32.503700 36.953700 17.561400 14.827000 21.503700 21.883300 19.993300 20.314600 19.848100 NaN 33.008900 33.027600 37.319900 41.112500 38.802800 34.001600 36.199500 25.953200 26.233700 27.360200 26.892100 19.894100 22.827200 23.456600 23.818100 26.915400 27.782700 27.521800 29.361900 30.733300 29.283300 27.413500 28.016900 26.887900 25.967200 27.681100 25.958700 27.906600 27.303100 28.059600 28.139400 28.010600 26.288300 25.956600
2.5% 30.804460 29.872500 29.651142 29.107265 26.725038 27.612838 33.503300 39.646380 22.932245 23.961500 23.622800 22.957500 21.856578 22.717668 22.503700 NaN 33.559130 33.559820 37.741800 42.052485 39.295412 34.551730 38.969000 28.077715 28.425050 28.986500 29.311735 21.428640 23.275745 24.597763 24.327258 29.105970 28.766383 28.843300 30.525600 33.093800 29.695445 28.155975 29.746625 28.851917 27.331400 29.074585 28.529245 30.746200 28.969940 29.749800 30.422663 29.794880 32.950192 35.415150
25% 32.082500 31.420000 31.282800 30.624050 30.578500 31.062600 36.095700 42.116600 24.994850 27.085800 26.132150 26.561800 24.369325 26.285100 25.507200 NaN 35.025700 35.033700 39.707400 43.558750 40.879075 36.720800 40.858400 29.861500 29.769575 30.243300 30.995875 27.004900 25.760600 27.183450 26.429600 31.214075 31.483975 31.275050 33.169650 37.715600 33.867500 32.783000 34.971575 29.922875 28.501675 30.281050 30.029200 32.967500 30.620100 30.944900 31.907000 31.221700 35.372375 39.436400
50% 32.996450 32.153300 32.235900 31.826800 31.598300 32.560900 37.408700 42.938400 26.359300 28.916500 27.770400 28.069400 26.571100 28.254350 27.173300 NaN 35.660800 36.064800 40.559600 44.302500 41.679900 37.745200 41.544700 30.889100 30.894400 30.993300 31.694300 31.372800 30.660250 30.584800 30.425200 33.383100 33.399950 33.386500 35.394400 41.479100 37.929100 35.411700 37.232400 30.964150 29.603600 31.433700 31.309800 35.124700 32.899300 32.404400 33.300000 31.831800 36.793300 43.122500
75% 33.845525 33.074200 33.437225 32.785800 32.487800 34.734200 38.531500 44.184700 27.406850 29.664450 29.094550 28.883700 27.365450 29.147500 28.004000 NaN 36.541700 37.045150 41.181400 44.905800 42.415700 38.512100 42.289200 33.099900 32.563350 33.177500 34.531175 33.630000 32.812600 32.730550 32.830975 34.046825 33.661425 34.274250 36.838050 42.436800 38.776750 36.615925 38.208425 31.710175 30.548300 32.453200 32.230275 37.436450 34.135700 33.395600 34.949650 32.568200 38.722150 45.025400
97.5% 35.317377 35.402700 38.904422 34.430595 33.989800 36.677412 40.418200 47.713800 28.564540 32.014145 30.450630 30.605420 28.451600 30.280983 29.528200 NaN 38.597700 38.557300 43.828500 46.041900 43.448125 39.603460 43.914700 36.709955 36.417857 36.177880 38.666500 38.032700 37.033180 37.556213 37.164407 34.982897 34.639850 35.328800 38.395400 43.713310 39.792735 37.789600 39.213563 33.001500 31.583100 34.356045 34.390400 40.559425 36.973240 34.834300 37.556700 35.033660 39.795100 47.219650
max 52.632000 38.618900 41.422900 35.911900 35.323100 39.079600 42.940300 57.927100 29.948900 39.014900 31.493000 35.047000 35.322100 34.489900 41.200600 NaN 39.372500 39.382500 44.773100 46.874400 44.323600 40.154400 45.457600 37.511100 37.345300 36.747400 40.684300 39.920500 39.203900 39.029200 39.804000 35.190800 35.251100 35.496000 38.875300 44.680600 40.700400 38.527700 41.145600 36.261100 32.708100 35.855200 37.102600 43.767500 41.157400 38.849200 39.883400 37.128000 41.709400 48.794700
OptRev_Tilt(da)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 32.433031 30.631897 32.819397 30.766198 31.015674 30.680011 32.991855 38.182560 27.153895 27.496116 26.509645 27.105094 26.558860 27.493676 26.807097 NaN 36.095717 36.766594 41.460805 44.048905 41.896432 37.856348 40.553512 31.801593 31.326927 31.785395 32.839315 30.731251 29.882947 30.378875 30.051633 33.230512 32.748598 33.439831 35.477095 39.672397 36.640125 33.802405 35.413204 31.461681 30.222023 31.802937 31.404340 35.812696 33.000338 32.404383 33.423327 32.095449 33.936152 36.809625
std 1.190383 1.365908 1.327217 1.341794 1.537130 1.552760 1.720233 2.079906 1.629645 1.657036 1.734656 2.106024 1.591909 1.969213 1.579673 NaN 1.142661 1.249490 1.413307 1.147314 1.075551 1.175765 1.171746 2.551486 2.425534 1.992850 2.364283 4.753803 3.972376 3.441364 3.815527 2.032460 1.743350 2.062003 2.712579 3.105415 2.661978 2.725342 2.335743 1.399396 1.369956 1.384809 1.503719 2.629442 2.183649 1.382497 1.695866 1.314577 1.218868 3.100074
min 29.340800 27.592800 29.629200 27.449100 25.430500 26.079300 28.481500 33.660700 20.065000 21.542500 20.845500 13.562100 22.030900 19.448500 21.152200 NaN 33.052900 33.693800 38.209100 41.187100 39.217000 34.656000 37.164200 27.084500 26.168800 27.757500 28.284400 20.604400 23.424900 24.164800 23.523100 27.496300 27.894000 28.045800 28.921200 30.589300 30.328200 26.745600 28.045500 27.375300 26.092500 27.924700 27.236000 28.861800 28.602800 28.281800 27.891000 29.295100 30.260600 30.339600
2.5% 30.093637 28.344400 30.395188 28.162050 27.038660 26.903613 29.218360 34.612200 23.792285 24.167290 22.584300 22.700220 23.058705 23.063207 23.555700 NaN 33.900930 34.321300 38.741100 41.808170 39.860600 35.092500 37.707800 28.407700 28.085693 29.228960 29.619187 21.731580 23.679503 25.002775 24.264900 29.516320 28.955715 29.453300 30.282400 32.735140 31.191400 27.824187 29.316525 29.123000 27.775487 29.467600 28.902435 31.879787 29.685500 29.978850 30.678850 30.174900 32.151895 32.698600
25% 31.613400 29.602000 31.901800 30.006300 30.360575 29.813075 31.917000 36.800200 26.145300 26.426600 25.373750 26.079700 25.435400 26.253175 25.735400 NaN 35.459400 35.867400 40.514400 43.275275 41.149525 37.080400 39.894500 30.006150 29.550475 30.245500 31.130075 26.811800 26.011575 27.622925 26.625125 31.495725 31.771225 31.562750 33.078125 37.530325 34.016750 31.240725 33.872150 30.199150 29.029700 30.603850 30.103900 33.635875 30.939600 31.178800 31.987100 31.157400 33.053100 34.146600
50% 32.503750 30.605100 32.829500 30.854500 31.282450 30.757400 33.253900 38.111600 27.259100 27.898500 26.777200 27.697500 27.041500 28.087750 27.270300 NaN 35.974800 36.864600 41.501600 44.057000 41.997000 38.025300 40.582200 30.898600 30.651950 31.161400 32.067950 31.821450 30.709850 30.723550 30.402800 34.189900 33.765650 34.379700 36.667450 41.175600 37.852900 35.087100 36.214950 31.589900 30.474600 31.928400 31.655500 35.654200 33.293400 32.633900 33.370500 31.766000 33.701300 36.547200
75% 33.324225 31.605900 33.717950 31.626300 32.006950 31.690625 34.314300 39.579600 28.508150 28.723800 27.950350 28.525700 27.789025 28.984500 27.983850 NaN 36.688600 37.723250 42.192200 44.824125 42.686100 38.640400 41.354600 33.833500 32.621300 32.944000 34.502825 34.671100 32.812700 32.770175 32.795175 34.882100 34.008425 35.240675 37.800700 41.859700 38.420800 36.044200 36.886550 32.581650 31.273825 32.867500 32.506325 37.498500 34.438300 33.522000 34.651500 32.836700 34.512200 38.337900
97.5% 34.697052 33.708900 35.343955 33.179060 33.330000 33.400500 35.664300 42.293100 29.589710 29.853560 29.037400 30.012900 28.760005 29.986728 29.119240 NaN 38.875460 39.273955 44.616600 46.418548 43.681637 39.901600 42.665363 37.415685 36.849260 36.468360 38.080563 38.389755 36.796797 37.423275 37.036345 35.555665 35.075135 35.715800 39.254500 42.971477 40.297300 36.673800 38.044787 33.946085 32.545500 34.441500 34.360100 41.356437 37.040400 34.689250 36.600800 34.891200 36.878600 43.571250
max 35.424400 37.329800 37.548200 35.061000 34.704800 34.965200 36.711600 46.326700 33.128000 33.485800 29.735300 32.724300 30.907500 32.802500 33.899200 NaN 39.716500 40.364000 45.267600 47.144900 44.151100 40.659100 44.216700 38.583500 37.990400 37.576600 40.078100 40.340200 39.233500 39.067900 38.823700 36.693000 35.370800 37.234400 39.818800 43.293700 40.701200 37.206900 38.787600 35.555800 33.704400 35.304700 36.418400 43.602900 39.317600 37.613600 38.356800 36.087000 38.077800 44.939500
OptRev_Tilt(rt)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2203.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.00000 1570.000000 1570.000000 1563.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 32.862700 31.960747 32.392670 31.690125 31.172152 31.766336 36.439825 41.462049 26.099639 28.348577 27.505172 27.57895 25.913518 27.643429 26.733333 NaN 35.791992 35.995246 40.585806 44.246919 41.600065 37.547775 41.469155 31.555894 31.412097 31.737759 32.773729 30.436923 29.753192 30.199193 30.033287 32.551441 32.457167 32.818334 34.833949 40.008777 36.576465 34.237206 36.268685 30.903334 29.530794 31.446388 31.203570 35.319856 32.650817 32.247545 33.540068 32.307866 36.100192 38.710147
std 1.250700 1.304190 1.847937 1.383531 1.602483 1.811785 1.552193 1.433022 1.574081 2.226812 1.885069 1.96881 1.918536 2.121665 1.834009 NaN 1.185922 1.290526 1.437980 1.045128 1.096856 1.252827 1.142840 2.263263 2.246465 1.923680 2.452420 4.389655 4.096931 3.455796 3.789929 1.934298 1.740755 1.882429 2.321702 3.287919 2.871999 2.753653 2.457807 1.184946 1.218671 1.435750 1.533717 2.782969 2.286188 1.466995 1.993674 1.270149 1.805027 1.883778
min 29.915200 28.911500 28.717700 28.442900 25.063300 26.291200 31.896100 36.362300 16.872900 15.102900 21.505000 22.00310 20.373900 20.207700 19.755100 NaN 33.009800 33.027600 37.319900 41.142000 38.810400 34.027300 38.382600 26.898900 26.255700 27.360000 28.244800 19.938400 22.947800 23.456400 23.857700 26.883300 27.761200 27.513300 29.355100 30.733600 29.280300 27.417100 27.997900 27.415600 25.968500 27.679800 25.958700 27.906300 27.386700 28.059200 28.139400 28.695200 30.375100 28.617500
2.5% 30.618153 29.593400 29.710100 29.089950 26.752715 27.730337 33.059340 38.203560 22.970145 23.952410 23.578750 22.84280 21.856478 22.744630 22.508300 NaN 33.571700 33.559820 37.741800 42.062385 39.288800 34.563000 38.973200 28.466085 28.402815 29.090120 29.842225 21.436183 23.289538 24.686737 24.448100 29.116850 28.734390 28.862200 30.550200 33.082600 29.821475 28.181500 29.720750 28.905500 27.335675 29.070590 28.529245 30.820187 28.971560 29.825950 30.451825 30.386220 32.574055 34.743000
25% 31.969150 31.103100 31.228400 30.723400 30.467375 30.629600 35.391500 40.743900 24.989950 27.051200 26.133050 26.55460 24.386625 26.289975 25.540650 NaN 35.025400 35.022400 39.707400 43.573075 40.862775 36.698800 40.833750 29.913250 29.769925 30.329500 31.116275 26.953975 25.725300 27.220500 26.456000 31.237250 31.471200 31.289700 33.160475 37.713700 33.836800 32.657725 34.850225 29.944375 28.502225 30.279700 30.029600 32.995700 30.621000 30.944200 31.906900 31.464100 34.697200 37.843300
50% 32.864500 31.871500 32.144500 31.692100 31.404500 31.822400 36.650500 41.635800 26.309600 28.927600 27.772300 28.06870 26.567400 28.296400 27.223300 NaN 35.618200 36.064800 40.559600 44.327900 41.673700 37.741000 41.499100 30.942300 30.907850 31.019400 31.963200 31.371050 30.664500 30.757700 30.453200 33.374850 33.387600 33.432750 35.564900 41.485050 37.923700 34.793150 37.116850 30.989850 29.594150 31.447100 31.314250 35.150100 32.886000 32.420800 33.290850 32.040100 35.914800 38.732200
75% 33.704500 32.796400 33.297100 32.663050 32.215225 33.052100 37.484200 42.374400 27.373300 29.653200 29.093250 28.86750 27.365350 29.168375 28.042250 NaN 36.541700 37.046500 41.181400 44.929500 42.428050 38.531600 42.165200 33.050900 32.713150 33.113000 34.606625 33.580950 32.884750 32.738025 32.819100 34.066625 33.616050 34.279700 36.791800 42.433325 38.735700 36.542700 38.160100 31.745050 30.553800 32.458400 32.234250 37.434650 34.113900 33.406300 34.923900 32.966300 37.827750 39.902600
97.5% 35.128905 34.948100 38.055503 34.275715 33.615600 34.890000 39.129800 43.948660 28.543425 32.000005 30.500315 30.59834 28.456400 30.288245 29.524600 NaN 38.597700 38.557200 43.832500 46.065195 43.471500 39.652200 43.615350 36.671900 36.599592 36.157110 38.624203 37.992842 37.003872 37.120050 37.125565 35.059595 34.567905 35.320100 38.376200 43.697928 39.770275 37.328025 38.925700 33.017900 31.540588 34.370385 34.379200 40.598562 36.923740 34.818500 37.433200 35.249280 38.765000 42.429000
max 42.595700 36.629200 40.893700 35.661200 34.667100 36.427900 41.413900 45.690200 29.940900 39.014500 31.521800 35.04380 35.281600 34.358700 36.360100 NaN 39.372500 39.382200 44.776900 46.907700 44.341200 40.162000 44.720300 37.499400 37.480500 36.636600 40.537000 39.611100 39.216800 38.838500 38.916200 35.236600 35.113500 35.485700 38.853300 44.658600 40.663900 38.281600 39.908500 36.327700 32.544200 35.855200 37.102600 43.676600 41.121500 38.366900 39.743900 37.067900 40.755500 44.551500

CF and revenue impacts

In [33]:
########## Baseline = mustrun
### Data-indexed parameters
data = [
    'CF_OptRev/OptCF_hist,da,f,mustrun',
    'CF_OptRev/OptCF_hist,rt,f,mustrun',
    'CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun',
    'CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
    'Rev_OptRev/OptCF_hist,da,f,mustrun',
    'Rev_OptRev/OptCF_hist,rt,f,mustrun',
    'Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun',
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
]
colindex = [0, 0, 1, 1, 2, 2, 3, 3,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',
             'left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],
         mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),
            slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [0.63, 1.02],
    [0.63, 1.02],
    [0.98,1.37],
    [0.98,1.37],
]
xlim = [
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [0.1, 0.1, 0.1, 0.1]
minlocs = [2, 2, 2, 2,]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue',
    'Revenue',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [2, 2, 2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Revenue-opt. vs. CF-opt., must-run baseline', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
In [34]:
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
CF_OptRev/OptCF_hist,da,f,mustrun CF_OptRev/OptCF_hist,rt,f,mustrun CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun Rev_OptRev/OptCF_hist,da,f,mustrun Rev_OptRev/OptCF_hist,rt,f,mustrun Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
count 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000 2209.000000
mean 0.935713 0.896420 0.890050 0.781320 1.042364 1.130232 1.053642 1.224193
std 0.017484 0.023111 0.028419 0.028541 0.018882 0.039487 0.033171 0.101965
min 0.821078 0.774044 0.696302 0.640909 0.993515 1.032940 0.998301 1.105384
2.5% 0.900771 0.856013 0.815125 0.715304 1.005221 1.058584 1.010603 1.129142
25% 0.926839 0.883491 0.885285 0.765099 1.033260 1.110689 1.041737 1.168903
50% 0.934574 0.895030 0.895518 0.788514 1.043464 1.128227 1.051820 1.196672
75% 0.942720 0.910913 0.904803 0.803383 1.052679 1.146930 1.060932 1.244334
97.5% 0.974518 0.945262 0.930582 0.817736 1.077262 1.202694 1.110678 1.420512
max 0.987178 0.966279 0.941928 0.841168 1.222468 1.414609 1.559459 2.159810
median
CF_OptRev/OptCF_hist,da,f,mustrun CF_OptRev/OptCF_hist,rt,f,mustrun CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun Rev_OptRev/OptCF_hist,da,f,mustrun Rev_OptRev/OptCF_hist,rt,f,mustrun Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 0.998748 NaN 0.999933 0.999281 0.997796 0.998742 NaN 0.997344 NaN NaN 0.999289 0.997175 0.999025 NaN 0.998659 NaN 0.999920 0.999082 0.997796 0.998638 NaN 0.991157 NaN NaN 0.994048 0.995121 0.997210 NaN 1.001266 NaN 1.001588 1.002546 1.001635 1.003360 NaN 1.002519 NaN NaN 1.002033 1.002441 1.003203 NaN 1.001283 NaN 1.001588 1.002617 1.001635 1.003387 NaN 1.005564 NaN NaN 1.003906 1.007934 1.004289 NaN
2011 0.998575 0.965722 1.000060 0.998788 0.997443 0.998193 NaN 0.997797 0.974514 0.999944 0.999063 0.997963 0.997861 NaN 0.997985 0.964373 1.000053 0.998698 0.997443 0.998148 NaN 0.954237 0.971661 0.999117 0.993504 0.995763 0.994846 NaN 1.001628 1.051259 1.000664 1.002493 1.001098 1.003984 NaN 1.002440 1.034713 1.000401 1.002398 1.001928 1.005683 NaN 1.001747 1.051538 1.000665 1.002526 1.001098 1.004008 NaN 1.011347 1.037183 1.000432 1.005653 1.002891 1.006597 NaN
2012 0.995396 0.982514 1.000151 0.997335 0.997441 0.998510 NaN 0.993732 0.989014 1.000105 0.998778 0.996178 0.998416 NaN 0.995232 0.982129 1.000141 0.996983 0.997441 0.998486 NaN 0.987609 0.987500 0.999711 0.994395 0.995681 0.997941 NaN 1.005298 1.024391 1.001971 1.002306 1.001893 1.002001 NaN 1.007272 1.013150 1.002076 1.001401 1.003314 1.002665 NaN 1.005423 1.024493 1.001983 1.002849 1.001913 1.002043 NaN 1.017422 1.014763 1.002134 1.005236 1.004770 1.002963 NaN
2013 0.997552 0.994285 0.996971 0.999728 0.998587 0.999346 NaN 0.997202 0.994854 0.997923 0.999652 0.997300 0.999646 NaN 0.997444 0.994234 0.996971 0.999489 0.998585 0.999339 NaN 0.985819 0.993857 0.997194 0.996086 0.996498 0.999391 NaN 1.000617 1.008389 1.002587 1.000326 1.001286 1.002257 NaN 1.002618 1.008552 1.000911 1.000352 1.003391 1.001985 NaN 1.000655 1.008450 1.002590 1.000540 1.001414 1.002271 NaN 1.013285 1.009234 1.000928 1.002497 1.006027 1.002251 NaN
2014 0.995700 0.997577 0.991220 0.999496 0.993239 0.998816 NaN 0.990090 0.999400 0.990672 0.999722 0.992544 0.999025 NaN 0.992633 0.997415 0.991199 0.999027 0.993239 0.998762 NaN 0.960239 0.998269 0.980942 0.996386 0.990766 0.997557 NaN 1.003777 1.006847 1.003339 1.000284 1.001075 1.000043 NaN 1.011600 1.002867 1.001802 1.000302 1.000994 0.999981 NaN 1.003891 1.006913 1.003339 1.000545 1.001075 1.000059 NaN 1.030792 1.003326 1.001974 1.002085 1.002304 1.000538 NaN
2015 0.990244 0.986939 0.996699 0.999311 0.999161 0.999762 0.994087 0.978461 0.997624 0.996650 0.999324 0.998190 0.999864 0.996920 0.987560 0.986779 0.996658 0.998959 0.999124 0.999710 0.994064 0.931639 0.994492 0.994545 0.994858 0.996215 0.997973 0.953715 1.004217 1.023337 1.002844 1.001603 1.001855 1.000930 1.002896 1.020153 1.005624 1.005136 1.001028 1.003692 1.000788 1.002752 1.004822 1.023361 1.002846 1.001982 1.001864 1.000953 1.002896 1.046212 1.006872 1.006976 1.005214 1.006242 1.002160 1.066360
2016 0.983132 0.992566 0.999431 0.997871 0.997944 0.998749 0.985828 0.961154 0.993484 0.996959 0.998613 0.995692 0.998833 0.970385 0.978965 0.992310 0.999430 0.997587 0.997890 0.998666 0.985722 0.866740 0.991067 0.989202 0.994128 0.991426 0.997813 0.881089 1.010591 1.009690 1.002196 1.002379 1.002593 1.002218 1.008413 1.046734 1.008081 1.006293 1.001751 1.004609 1.002542 1.034590 1.011377 1.009805 1.002196 1.002493 1.002609 1.002255 1.008861 1.103048 1.010248 1.016362 1.004832 1.009081 1.003270 1.135252
2017 0.934574 0.994648 0.999301 0.998500 0.999544 0.999214 0.950324 0.895030 0.994937 0.998242 0.998846 0.999012 0.998726 0.869636 0.895518 0.994245 0.999206 0.998117 0.999412 0.999177 0.891771 0.788514 0.991269 0.992823 0.995671 0.994513 0.997632 0.778472 1.043464 1.007203 1.003913 1.003146 1.002299 1.001424 1.023485 1.128227 1.006908 1.006121 1.003103 1.004494 1.002649 1.160020 1.051820 1.007433 1.003940 1.003329 1.002299 1.001442 1.031005 1.196672 1.009585 1.009864 1.005275 1.006725 1.003223 1.284267
max
CF_OptRev/OptCF_hist,da,f,mustrun CF_OptRev/OptCF_hist,rt,f,mustrun CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun Rev_OptRev/OptCF_hist,da,f,mustrun Rev_OptRev/OptCF_hist,rt,f,mustrun Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 1.000885 NaN 1.000979 1.000870 1.001024 1.002165 NaN 1.000612 NaN NaN 1.000925 1.001009 1.002160 NaN 1.000764 NaN 1.000979 1.000864 1.001024 1.002165 NaN 0.995601 NaN NaN 1.000400 0.999439 1.001215 NaN 1.005628 NaN 1.003642 1.007356 1.005632 1.009767 NaN 1.036135 NaN NaN 1.007889 1.008830 1.011587 NaN 1.009463 NaN 1.003642 1.034138 1.005632 1.092097 NaN 1.291673 NaN NaN 1.092822 1.033603 1.460574 NaN
2011 1.000464 0.992659 1.000948 1.000680 1.000713 1.001381 NaN 1.001070 0.998827 1.000560 1.000492 1.000518 1.001151 NaN 0.999805 0.982423 1.000948 1.000680 1.000713 1.001381 NaN 0.961598 0.991314 0.999678 0.998976 0.999897 1.000382 NaN 1.024543 1.093176 1.002037 1.005043 1.006721 1.011163 NaN 1.024102 1.052069 1.002406 1.005891 1.007643 1.018701 NaN 1.028702 1.242170 1.002037 1.010022 1.006721 1.056405 NaN 1.131301 1.138445 1.016929 1.066993 1.016984 1.113820 NaN
2012 1.000136 0.994097 1.001589 1.000779 1.000575 1.000563 NaN 1.000158 0.997603 1.001923 1.000904 1.000542 1.000546 NaN 0.999821 0.994097 1.001589 1.000779 1.000575 1.000563 NaN 0.996629 0.996632 1.001381 1.000343 0.999772 1.000388 NaN 1.031087 1.134148 1.007462 1.007640 1.014103 1.009528 NaN 1.187379 1.089397 1.008574 1.007198 1.015180 1.018793 NaN 1.071279 1.514284 1.007462 1.107741 1.014103 1.042595 NaN 1.253264 1.114905 1.016805 1.371152 1.028847 1.110387 NaN
2013 1.000394 0.998447 0.998488 1.000967 1.002098 1.001326 NaN 1.000800 0.999948 0.999369 1.000720 1.003276 1.001118 NaN 1.000180 0.998447 0.998488 1.000967 1.002098 1.001326 NaN 0.991689 0.999603 0.998622 1.000233 1.001218 1.001118 NaN 1.021769 1.052779 1.005178 1.003818 1.006411 1.006031 NaN 1.016021 1.063175 1.003808 1.008644 1.014256 1.021508 NaN 1.021894 1.060958 1.005979 1.106700 1.006411 1.034658 NaN 1.082055 1.115892 1.004362 1.466804 1.063301 1.072655 NaN
2014 1.000641 1.000869 0.995355 1.000447 0.999747 1.000945 NaN 0.999862 1.001532 0.995070 1.000726 0.999739 1.000961 NaN 0.999313 1.000869 0.995355 1.000447 0.999747 1.000945 NaN 0.974773 1.000699 0.987913 1.000622 0.998229 1.000742 NaN 1.027635 1.029071 1.009941 1.015850 1.007347 1.005186 NaN 1.043214 1.028569 1.007982 1.019880 1.008567 1.004961 NaN 1.027648 1.073666 1.009941 1.065326 1.007347 1.030248 NaN 1.166288 1.059748 1.008139 1.420097 1.053436 1.150027 NaN
2015 0.998203 0.993558 0.999503 1.001492 1.002645 1.001623 0.999814 0.998154 1.000206 1.000978 1.000674 1.002823 1.001383 1.000216 0.998203 0.993558 0.999309 1.001492 1.002645 1.001623 0.999814 0.951362 0.999391 0.998863 1.000246 0.998994 1.001145 0.963234 1.069368 1.038548 1.014036 1.010807 1.005598 1.004477 1.012461 1.106972 1.060230 1.010324 1.031035 1.016377 1.013788 1.009446 1.069368 1.044821 1.014037 1.072182 1.005598 1.140447 1.012461 1.338491 1.068929 1.011737 1.334974 1.085185 1.266746 1.143931
2016 0.997060 0.996633 1.001765 1.001313 1.000533 1.001238 1.000592 0.989867 0.999290 1.001243 1.001278 1.001123 1.001243 0.999775 0.997060 0.996444 1.001765 1.001313 1.000436 1.001238 1.000347 0.898813 0.996503 0.993100 1.001038 0.998722 1.000344 0.913883 1.030858 1.041762 1.006216 1.008950 1.012860 1.009045 1.031370 1.174392 1.044713 1.014900 1.014916 1.029832 1.012171 1.120918 1.076988 1.097889 1.024954 1.023332 1.012860 1.038580 1.031381 1.443184 1.125101 1.074771 1.126758 1.261031 1.156913 1.342019
2017 0.987178 0.999898 1.001463 1.000558 1.001635 1.001686 0.999567 0.966279 0.999976 1.001362 1.000717 1.001731 1.001927 0.998234 0.941928 0.999747 1.001463 1.000558 1.001635 1.001686 0.948706 0.841168 0.999094 0.996927 1.000707 1.000436 1.000858 0.906871 1.222468 1.035323 1.010683 1.011823 1.005270 1.016667 1.082011 1.414609 1.035030 1.014126 1.013034 1.018715 1.033491 1.310973 1.559459 1.082642 1.192813 1.037185 1.005270 1.017851 1.086942 2.159810 1.347413 1.467675 1.247010 1.216046 1.093964 2.087167
CF_OptRev/OptCF_hist,da,f,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.998796 0.998487 0.994592 0.997408 0.995259 0.988713 0.982427 0.935713 0.966208 0.981868 0.993702 0.997210 0.986896 0.992160 0.994420 0.999888 1.000027 1.000016 0.996843 0.991127 0.996739 0.999449 0.999312 0.999154 0.998784 0.997456 0.999583 0.998737 0.998970 0.997467 0.997970 0.998194 0.997728 0.996784 0.998655 0.994335 0.999219 0.997238 0.999478 0.998691 0.997924 0.998425 0.999302 0.998204 0.999596 0.998523 0.999063 0.993927 0.986260 0.949781
std 0.000854 0.001168 0.003908 0.001275 0.002663 0.009309 0.005395 0.017484 0.005558 0.006738 0.003561 0.002017 0.002285 0.002210 0.002694 0.000495 0.000325 0.000778 0.000905 0.001262 0.001148 0.000744 0.000798 0.000904 0.000778 0.001326 0.000969 0.002574 0.001221 0.001947 0.001651 0.001780 0.001847 0.002881 0.001442 0.002801 0.001217 0.002325 0.000906 0.001302 0.001396 0.000758 0.000741 0.001856 0.000846 0.001122 0.000830 0.002407 0.005329 0.014940
min 0.994485 0.983943 0.970262 0.979743 0.975555 0.918174 0.954858 0.821078 0.935534 0.898403 0.959453 0.977599 0.978384 0.970989 0.974496 0.998227 0.998934 0.997553 0.993021 0.988006 0.994074 0.997085 0.996659 0.995694 0.996260 0.988833 0.991461 0.984061 0.995229 0.992267 0.993348 0.993677 0.992379 0.988670 0.994327 0.988252 0.994265 0.986846 0.996455 0.993131 0.993317 0.993912 0.996194 0.988883 0.993550 0.992489 0.991296 0.978781 0.967264 0.901300
2.5% 0.997000 0.997062 0.981131 0.994868 0.988660 0.963387 0.969911 0.900771 0.956369 0.967658 0.983358 0.993070 0.982318 0.986915 0.988583 0.998703 0.999312 0.998202 0.994823 0.988580 0.994844 0.998000 0.997794 0.997545 0.996996 0.995249 0.998378 0.988304 0.995849 0.993325 0.993927 0.995413 0.994039 0.989467 0.995275 0.990654 0.996430 0.991622 0.997652 0.996125 0.994905 0.996680 0.997828 0.992948 0.997692 0.995987 0.997343 0.988626 0.976825 0.922247
25% 0.998253 0.998202 0.993816 0.996829 0.994232 0.986914 0.979571 0.926839 0.962453 0.980647 0.993180 0.996352 0.985333 0.991407 0.993451 0.999616 0.999881 0.999613 0.996305 0.990333 0.995761 0.999011 0.998736 0.998606 0.998380 0.996779 0.999281 0.998883 0.998109 0.996101 0.996927 0.997005 0.996070 0.996785 0.997814 0.992124 0.998520 0.997359 0.998972 0.997786 0.996951 0.998009 0.998797 0.997684 0.999070 0.997846 0.998523 0.992616 0.982588 0.939064
50% 0.998748 0.998575 0.995396 0.997552 0.995700 0.990244 0.983132 0.934574 0.965722 0.982514 0.994285 0.997577 0.986939 0.992566 0.994648 0.999933 1.000060 1.000151 0.996971 0.991220 0.996699 0.999431 0.999301 0.999281 0.998788 0.997335 0.999728 0.999496 0.999311 0.997871 0.998500 0.997796 0.997443 0.997441 0.998587 0.993239 0.999161 0.997944 0.999544 0.998742 0.998193 0.998510 0.999346 0.998816 0.999762 0.998749 0.999214 0.994087 0.985828 0.950324
75% 0.999440 0.998987 0.996528 0.998272 0.996746 0.993780 0.985903 0.942720 0.969907 0.984632 0.995235 0.998447 0.988545 0.993382 0.995999 1.000222 1.000229 1.000569 0.997512 0.991863 0.997591 0.999891 0.999846 0.999805 0.999248 0.998428 1.000017 0.999918 0.999810 0.998924 0.999217 0.999837 0.999478 0.998629 0.999899 0.997184 1.000100 0.998523 1.000068 0.999713 0.998943 0.998920 0.999857 0.999478 1.000217 0.999322 0.999642 0.995444 0.989300 0.959381
97.5% 1.000312 0.999628 0.999052 0.999455 0.999034 0.996649 0.991407 0.974518 0.978213 0.989772 0.996992 0.999742 0.991056 0.995188 0.998049 1.000753 1.000536 1.001276 0.998200 0.993754 0.999137 1.000977 1.001101 1.000710 1.000420 0.999853 1.000539 1.000287 1.000788 1.000715 1.000219 1.000829 1.000400 0.999797 1.001271 0.999528 1.001163 0.999591 1.001194 1.000874 1.000207 0.999723 1.000622 1.000029 1.000796 1.000231 1.000346 0.998098 0.996943 0.985055
max 1.000885 1.000464 1.000136 1.000394 1.000641 0.998203 0.997060 0.987178 0.992659 0.994097 0.998447 1.000869 0.993558 0.996633 0.999898 1.000979 1.000948 1.001589 0.998488 0.995355 0.999503 1.001765 1.001463 1.000870 1.000680 1.000779 1.000967 1.000447 1.001492 1.001313 1.000558 1.001024 1.000713 1.000575 1.002098 0.999747 1.002645 1.000533 1.001635 1.002165 1.001381 1.000563 1.001326 1.000945 1.001623 1.001238 1.001686 0.999814 1.000592 0.999567
CF_OptRev/OptCF_hist,rt,f,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.997275 0.997449 0.988007 0.996816 0.989203 0.974862 0.957673 0.896420 0.974435 0.987381 0.994134 0.999015 0.997136 0.992793 0.994352 NaN 0.999948 0.999971 0.997789 0.990734 0.996829 0.997013 0.998173 0.999351 0.998895 0.998566 0.999481 0.999168 0.998760 0.997955 0.998323 0.997386 0.997230 0.996247 0.997567 0.993552 0.998045 0.993662 0.998389 0.998847 0.997177 0.998333 0.999097 0.998500 0.999590 0.998657 0.998627 0.996846 0.960948 0.893412
std 0.002789 0.002262 0.019639 0.002114 0.005117 0.012789 0.014243 0.023111 0.006919 0.008153 0.004200 0.001550 0.002289 0.003465 0.003191 NaN 0.000209 0.000877 0.000799 0.001400 0.001375 0.001710 0.001211 0.000748 0.000810 0.001197 0.001061 0.001896 0.001635 0.002066 0.001624 0.002483 0.001940 0.002634 0.002147 0.003324 0.002139 0.005308 0.002227 0.001323 0.002074 0.000886 0.002469 0.001731 0.000985 0.001085 0.001023 0.001501 0.025359 0.058190
min 0.943378 0.985167 0.875259 0.981692 0.955062 0.907179 0.882312 0.774044 0.954631 0.920048 0.947713 0.981910 0.960395 0.963861 0.973992 NaN 0.999055 0.994092 0.994131 0.987475 0.993118 0.990631 0.994321 0.995042 0.995949 0.991274 0.990711 0.987463 0.989015 0.991452 0.992398 0.991607 0.990888 0.988671 0.992264 0.987781 0.989481 0.975938 0.989050 0.994002 0.989546 0.991290 0.979759 0.988738 0.983633 0.991613 0.980791 0.983327 0.905662 0.740967
2.5% 0.993664 0.991076 0.916268 0.991084 0.978264 0.947777 0.924494 0.856013 0.961282 0.966363 0.984231 0.994886 0.991841 0.981638 0.986982 NaN 0.999572 0.998001 0.995845 0.988021 0.994274 0.993551 0.995906 0.998118 0.996962 0.996160 0.998342 0.992486 0.993467 0.993415 0.993302 0.993317 0.993386 0.990703 0.993290 0.989202 0.992397 0.980671 0.992422 0.996051 0.992427 0.996349 0.989486 0.994122 0.997226 0.996255 0.996579 0.993635 0.918875 0.808626
25% 0.996473 0.996487 0.989655 0.995678 0.986987 0.966972 0.950459 0.883491 0.969665 0.986726 0.993208 0.998623 0.996762 0.992013 0.992944 NaN 0.999843 0.999486 0.997442 0.989814 0.995825 0.995789 0.997292 0.998946 0.998461 0.997943 0.999209 0.999140 0.998074 0.996690 0.997640 0.995579 0.995500 0.994592 0.996442 0.990734 0.997398 0.993299 0.997910 0.997868 0.995659 0.997892 0.999292 0.997681 0.999099 0.998053 0.998062 0.996130 0.938267 0.844219
50% 0.997344 0.997797 0.993732 0.997202 0.990090 0.978461 0.961154 0.895030 0.974514 0.989014 0.994854 0.999400 0.997624 0.993484 0.994937 NaN 0.999944 1.000105 0.997923 0.990672 0.996650 0.996959 0.998242 0.999289 0.999063 0.998778 0.999652 0.999722 0.999324 0.998613 0.998846 0.997175 0.997963 0.996178 0.997300 0.992544 0.998190 0.995692 0.999012 0.999025 0.997861 0.998416 0.999646 0.999025 0.999864 0.998833 0.998726 0.996920 0.970385 0.869636
75% 0.998577 0.998950 0.996423 0.998419 0.992413 0.984150 0.967370 0.910913 0.979444 0.991251 0.996110 0.999916 0.998260 0.994660 0.996325 NaN 1.000062 1.000548 0.998318 0.991639 0.997810 0.998306 0.998981 0.999833 0.999350 0.999323 0.999980 0.999973 0.999836 0.999447 0.999399 0.999703 0.998774 0.998420 0.999235 0.997115 0.999346 0.996526 0.999894 0.999906 0.998774 0.998901 0.999961 0.999788 1.000263 0.999415 0.999315 0.997965 0.982880 0.947605
97.5% 1.000093 1.000247 0.999735 0.999876 0.996034 0.990318 0.978746 0.945262 0.987306 0.994515 0.998349 1.000521 0.999355 0.996653 0.999120 NaN 1.000313 1.001223 0.999057 0.993602 0.999689 1.000197 1.000670 1.000681 1.000303 1.000165 1.000381 1.000468 1.000527 1.000732 1.000340 1.000804 1.000168 1.000014 1.001749 0.999441 1.001338 0.999285 1.000857 1.000974 0.999941 0.999790 1.000485 1.000355 1.000797 1.000296 1.000181 0.999222 0.993547 0.988034
max 1.000612 1.001070 1.000158 1.000800 0.999862 0.998154 0.989867 0.966279 0.998827 0.997603 0.999948 1.001532 1.000206 0.999290 0.999976 NaN 1.000560 1.001923 0.999369 0.995070 1.000978 1.001243 1.001362 1.000925 1.000492 1.000904 1.000720 1.000726 1.000674 1.001278 1.000717 1.001009 1.000518 1.000542 1.003276 0.999739 1.002823 1.001123 1.001731 1.002160 1.001151 1.000546 1.001118 1.000961 1.001383 1.001243 1.001927 1.000216 0.999775 0.998234
CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.998629 0.997390 0.994000 0.997138 0.992286 0.986128 0.975258 0.890050 0.964369 0.981009 0.993507 0.996898 0.986716 0.991702 0.993368 0.999862 1.000018 0.999964 0.996817 0.991085 0.996689 0.999361 0.998683 0.998553 0.998318 0.994970 0.997186 0.997158 0.997464 0.996783 0.997201 0.998187 0.997728 0.996770 0.998432 0.994335 0.999163 0.997145 0.999443 0.998307 0.997824 0.998125 0.999179 0.998040 0.999340 0.998305 0.998911 0.993916 0.984469 0.894405
std 0.001072 0.003723 0.005647 0.001701 0.003015 0.010172 0.012293 0.028419 0.007056 0.009792 0.004005 0.002902 0.002410 0.003036 0.004721 0.000530 0.000348 0.000906 0.000938 0.001277 0.001140 0.001070 0.005175 0.003685 0.001755 0.011380 0.010034 0.007780 0.005624 0.003462 0.003938 0.001774 0.001847 0.002873 0.002002 0.002801 0.001270 0.002348 0.000949 0.003683 0.001936 0.002760 0.001954 0.002216 0.002457 0.002376 0.001458 0.002409 0.007164 0.017465
min 0.991658 0.927782 0.932775 0.978054 0.972427 0.918174 0.892388 0.696302 0.851269 0.714140 0.934740 0.951365 0.972641 0.954112 0.919628 0.997141 0.998204 0.992490 0.993021 0.988006 0.991329 0.982377 0.892283 0.955199 0.984417 0.871239 0.903485 0.876191 0.950602 0.952288 0.944006 0.993677 0.992379 0.988670 0.982667 0.988252 0.993520 0.986846 0.995336 0.914349 0.951535 0.927662 0.952675 0.974309 0.937725 0.953378 0.976430 0.978781 0.965471 0.813535
2.5% 0.996368 0.992921 0.978058 0.991997 0.983390 0.962322 0.948286 0.815125 0.951304 0.965778 0.981433 0.991581 0.981741 0.984017 0.982780 0.998500 0.999187 0.998160 0.994758 0.988545 0.994800 0.997599 0.996776 0.993543 0.993509 0.980460 0.984901 0.985170 0.982680 0.990538 0.990604 0.995413 0.994039 0.989467 0.995229 0.990654 0.995253 0.991622 0.997603 0.995060 0.994722 0.995439 0.997515 0.991788 0.996536 0.995447 0.996713 0.988626 0.972109 0.869722
25% 0.998131 0.997462 0.993667 0.996559 0.991110 0.981663 0.968339 0.885285 0.960942 0.980190 0.993052 0.996100 0.985154 0.991016 0.992504 0.999587 0.999869 0.999576 0.996278 0.990238 0.995746 0.998985 0.998635 0.998338 0.998205 0.995782 0.998682 0.997600 0.997524 0.995150 0.996263 0.997005 0.996070 0.996785 0.997787 0.992124 0.998507 0.997171 0.998926 0.997702 0.996888 0.997977 0.998782 0.997488 0.998964 0.997751 0.998443 0.992616 0.977810 0.885437
50% 0.998659 0.997985 0.995232 0.997444 0.992633 0.987560 0.978965 0.895518 0.964373 0.982129 0.994234 0.997415 0.986779 0.992310 0.994245 0.999920 1.000053 1.000141 0.996971 0.991199 0.996658 0.999430 0.999206 0.999082 0.998698 0.996983 0.999489 0.999027 0.998959 0.997587 0.998117 0.997796 0.997443 0.997441 0.998585 0.993239 0.999124 0.997890 0.999412 0.998638 0.998148 0.998486 0.999339 0.998762 0.999710 0.998666 0.999177 0.994064 0.985722 0.891771
75% 0.999351 0.998415 0.996416 0.998189 0.993806 0.993375 0.984364 0.904803 0.968621 0.984177 0.995199 0.998397 0.988404 0.993260 0.995669 1.000197 1.000224 1.000567 0.997486 0.991841 0.997546 0.999890 0.999831 0.999684 0.999107 0.997671 0.999893 0.999741 0.999767 0.998694 0.999063 0.999834 0.999478 0.998626 0.999770 0.997184 1.000098 0.998523 1.000068 0.999678 0.998920 0.998914 0.999848 0.999454 1.000195 0.999301 0.999632 0.995435 0.989151 0.904210
97.5% 1.000266 0.999162 0.998796 0.999414 0.996629 0.996366 0.990641 0.930582 0.975757 0.988432 0.996952 0.999735 0.990829 0.995007 0.997564 1.000753 1.000536 1.001276 0.998200 0.993754 0.999055 1.000947 1.001101 1.000697 1.000420 0.999584 1.000333 1.000257 1.000765 1.000715 1.000026 1.000829 1.000400 0.999797 1.001271 0.999528 1.001163 0.999550 1.001194 1.000868 1.000205 0.999690 1.000614 1.000028 1.000778 1.000213 1.000342 0.998098 0.996604 0.931223
max 1.000764 0.999805 0.999821 1.000180 0.999313 0.998203 0.997060 0.941928 0.982423 0.994097 0.998447 1.000869 0.993558 0.996444 0.999747 1.000979 1.000948 1.001589 0.998488 0.995355 0.999309 1.001765 1.001463 1.000864 1.000680 1.000779 1.000967 1.000447 1.001492 1.001313 1.000558 1.001024 1.000713 1.000575 1.002098 0.999747 1.002645 1.000436 1.001635 1.002165 1.001381 1.000563 1.001326 1.000945 1.001623 1.001238 1.001686 0.999814 1.000347 0.948706
CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.990817 0.952255 0.980815 0.984604 0.955739 0.903383 0.851808 0.781320 0.961689 0.979910 0.992010 0.997348 0.993827 0.987737 0.984961 NaN 0.999002 0.999486 0.996875 0.981360 0.994613 0.988500 0.990158 0.990829 0.988374 0.988810 0.988125 0.992211 0.991848 0.991987 0.993458 0.994932 0.995900 0.995231 0.994320 0.990935 0.993035 0.983676 0.990594 0.995781 0.994630 0.996855 0.998382 0.996394 0.996250 0.996467 0.996962 0.952964 0.867596 0.798585
std 0.010060 0.007122 0.020121 0.006498 0.011120 0.041499 0.032560 0.028541 0.031552 0.021080 0.006993 0.003822 0.003571 0.008086 0.016509 NaN 0.000460 0.001005 0.001893 0.002080 0.001418 0.003542 0.011083 0.009833 0.014072 0.017419 0.021684 0.015352 0.011226 0.008623 0.007472 0.001864 0.002092 0.002598 0.010776 0.005830 0.009098 0.016718 0.014225 0.005614 0.003748 0.005689 0.003568 0.003932 0.006920 0.005417 0.002960 0.004693 0.032158 0.052130
min 0.764028 0.911917 0.866171 0.949298 0.909114 0.810432 0.716126 0.640909 0.821193 0.772478 0.935146 0.952453 0.959092 0.949335 0.705733 NaN 0.996584 0.989940 0.981283 0.977059 0.991058 0.957374 0.900400 0.953892 0.916573 0.835960 0.821708 0.799411 0.911552 0.923591 0.939714 0.990161 0.986220 0.987253 0.926045 0.954808 0.947325 0.921824 0.926874 0.914513 0.959074 0.921996 0.952496 0.941077 0.907448 0.932611 0.967896 0.928684 0.795181 0.670143
2.5% 0.985821 0.929036 0.904338 0.965369 0.931191 0.829372 0.785184 0.715304 0.861428 0.917689 0.969437 0.988715 0.985665 0.965863 0.947622 NaN 0.997542 0.997498 0.993178 0.978098 0.991879 0.977712 0.957133 0.964715 0.947571 0.947620 0.918438 0.964625 0.957470 0.970087 0.970303 0.991627 0.991845 0.989172 0.938445 0.967902 0.951092 0.930942 0.931704 0.974768 0.985604 0.987598 0.987018 0.986340 0.982323 0.987011 0.987555 0.941011 0.807784 0.728264
25% 0.990052 0.950753 0.980286 0.982307 0.948887 0.859286 0.821576 0.765099 0.964506 0.982792 0.991720 0.997276 0.992173 0.985947 0.986380 NaN 0.998852 0.998981 0.996714 0.979981 0.993582 0.987853 0.991263 0.987115 0.987556 0.989107 0.989000 0.992413 0.990692 0.990285 0.992165 0.993531 0.994774 0.993565 0.994597 0.989795 0.993608 0.981508 0.990669 0.995127 0.993202 0.997011 0.998701 0.994922 0.995433 0.995943 0.996235 0.950280 0.852113 0.755940
50% 0.991157 0.954237 0.987609 0.985819 0.960239 0.931639 0.866740 0.788514 0.971661 0.987500 0.993857 0.998269 0.994492 0.991067 0.991269 NaN 0.999117 0.999711 0.997194 0.980942 0.994545 0.989202 0.992823 0.994048 0.993504 0.994395 0.996086 0.996386 0.994858 0.994128 0.995671 0.995121 0.995763 0.995681 0.996498 0.990766 0.996215 0.991426 0.994513 0.997210 0.994846 0.997941 0.999391 0.997557 0.997973 0.997813 0.997632 0.953715 0.881089 0.778472
75% 0.993029 0.956934 0.990423 0.988375 0.964902 0.939327 0.879409 0.803383 0.976843 0.990240 0.995569 0.998907 0.996185 0.992682 0.993168 NaN 0.999286 1.000147 0.997672 0.982313 0.995689 0.990377 0.993932 0.998081 0.996304 0.997284 0.998927 0.998411 0.998948 0.997054 0.997695 0.996100 0.997833 0.997238 0.997429 0.993578 0.997107 0.994632 0.997750 0.998587 0.997122 0.998570 0.999814 0.998798 0.999152 0.998695 0.998677 0.956702 0.890817 0.839451
97.5% 0.994753 0.960072 0.995376 0.991029 0.968144 0.945681 0.890594 0.817736 0.986346 0.994031 0.997467 0.999727 0.998498 0.994711 0.995514 NaN 0.999565 1.000852 0.998424 0.986226 0.997424 0.992078 0.995877 0.999423 0.998272 0.999267 1.000095 0.999846 0.999881 1.000189 0.999570 0.998533 0.998828 0.998990 0.999102 0.997824 0.998225 0.996602 0.999111 1.000316 0.999139 0.999450 1.000386 0.999927 1.000349 0.999725 0.999857 0.959228 0.902796 0.888745
max 0.995601 0.961598 0.996629 0.991689 0.974773 0.951362 0.898813 0.841168 0.991314 0.996632 0.999603 1.000699 0.999391 0.996503 0.999094 NaN 0.999678 1.001381 0.998622 0.987913 0.998863 0.993100 0.996927 1.000400 0.998976 1.000343 1.000233 1.000622 1.000246 1.001038 1.000707 0.999439 0.999897 0.999772 1.001218 0.998229 0.998994 0.998722 1.000436 1.001215 1.000382 1.000388 1.001118 1.000742 1.001145 1.000344 1.000858 0.963234 0.913883 0.906871
Rev_OptRev/OptCF_hist,da,f,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.001344 1.001853 1.006515 1.000761 1.004292 1.005681 1.010948 1.042364 1.050829 1.024779 1.008881 1.007083 1.023438 1.010143 1.008054 1.001691 1.000690 1.002030 1.002555 1.003705 1.003073 1.002293 1.004049 1.002629 1.002438 1.002602 1.000488 1.001315 1.002548 1.002628 1.003720 1.002020 1.001690 1.003215 1.001616 1.001882 1.002079 1.003215 1.002330 1.003344 1.004005 1.002209 1.002406 1.000175 1.000910 1.002366 1.001558 1.003008 1.008821 1.025389
std 0.000871 0.002095 0.004925 0.001000 0.003049 0.007238 0.004669 0.018882 0.007012 0.008004 0.004071 0.002174 0.002952 0.003213 0.003899 0.000649 0.000362 0.000912 0.000969 0.001840 0.001587 0.000970 0.001074 0.001411 0.001313 0.001640 0.000574 0.003011 0.002481 0.001776 0.002665 0.001371 0.001625 0.003684 0.001546 0.002154 0.001338 0.002580 0.001201 0.001237 0.001327 0.001157 0.001086 0.000788 0.000835 0.001277 0.000916 0.001538 0.004209 0.016033
min 0.999336 0.999226 0.999689 0.998285 0.998917 0.997706 0.996916 0.993515 1.003153 1.005186 1.003081 1.000202 1.013966 1.003978 1.000095 1.000307 0.999594 1.000015 0.998162 0.996743 0.998383 0.999769 0.998923 0.999403 0.999115 0.999094 0.999798 0.997854 0.999172 0.999546 0.998966 1.000054 0.999638 0.999541 0.999759 0.997136 0.999879 0.999760 1.000064 0.999897 0.999915 0.999678 0.999303 0.995723 0.997732 0.999648 0.999460 0.998515 0.997383 0.988532
2.5% 0.999825 0.999891 1.001293 0.999345 1.000983 0.999173 1.002508 1.005221 1.034801 1.013942 1.005134 1.004002 1.018019 1.005743 1.003525 1.000505 0.999929 1.000419 0.999268 1.000220 1.000878 1.000607 1.002386 1.000107 0.999978 1.000011 0.999837 0.999490 0.999907 0.999878 0.999524 1.000227 0.999835 1.000058 0.999947 0.998366 1.000196 1.000214 1.000630 1.001007 1.001696 1.000573 1.000551 0.999023 0.999266 1.000456 1.000233 1.000517 1.001328 1.000473
25% 1.000710 1.001041 1.003850 1.000210 1.002496 1.002432 1.007936 1.033260 1.047552 1.022108 1.007421 1.005919 1.021463 1.008484 1.005990 1.001230 1.000531 1.001547 1.002129 1.002628 1.002045 1.001639 1.003412 1.001675 1.001391 1.001353 1.000129 0.999957 1.000752 1.001450 1.001902 1.000954 1.000534 1.000857 1.000396 1.000443 1.000803 1.001317 1.001200 1.002499 1.003091 1.001425 1.001596 0.999800 1.000339 1.001374 1.001010 1.002091 1.006288 1.012867
50% 1.001266 1.001628 1.005298 1.000617 1.003777 1.004217 1.010591 1.043464 1.051259 1.024391 1.008389 1.006847 1.023337 1.009690 1.007203 1.001588 1.000664 1.001971 1.002587 1.003339 1.002844 1.002196 1.003913 1.002546 1.002493 1.002306 1.000326 1.000284 1.001603 1.002379 1.003146 1.001635 1.001098 1.001893 1.001286 1.001075 1.001855 1.002593 1.002299 1.003360 1.003984 1.002001 1.002257 1.000043 1.000930 1.002218 1.001424 1.002896 1.008413 1.023485
75% 1.001889 1.002258 1.006884 1.001184 1.005314 1.006613 1.013577 1.052679 1.054676 1.026336 1.009224 1.007853 1.025263 1.011082 1.008563 1.002116 1.000897 1.002434 1.003068 1.004712 1.003708 1.002819 1.004573 1.003503 1.003377 1.003758 1.000727 1.000875 1.003693 1.003536 1.004808 1.002836 1.002327 1.002693 1.001980 1.003592 1.003151 1.003601 1.003278 1.004109 1.004755 1.002756 1.003178 1.000357 1.001426 1.003158 1.001962 1.003685 1.011032 1.036245
97.5% 1.003143 1.004323 1.023274 1.002684 1.013509 1.019296 1.021914 1.077262 1.063472 1.039740 1.019471 1.011714 1.029595 1.019061 1.018603 1.002875 1.001463 1.004832 1.004290 1.007505 1.006097 1.004551 1.006162 1.005091 1.004752 1.006246 1.001552 1.012609 1.009159 1.007292 1.010264 1.004957 1.005996 1.012651 1.005368 1.006210 1.005276 1.009954 1.004332 1.005922 1.006750 1.005088 1.004629 1.002516 1.002597 1.005262 1.003452 1.006160 1.018403 1.059692
max 1.005628 1.024543 1.031087 1.021769 1.027635 1.069368 1.030858 1.222468 1.093176 1.134148 1.052779 1.029071 1.038548 1.041762 1.035323 1.003642 1.002037 1.007462 1.005178 1.009941 1.014036 1.006216 1.010683 1.007356 1.005043 1.007640 1.003818 1.015850 1.010807 1.008950 1.011823 1.005632 1.006721 1.014103 1.006411 1.007347 1.005598 1.012860 1.005270 1.009767 1.011163 1.009528 1.006031 1.005186 1.004477 1.009045 1.016667 1.012461 1.031370 1.082011
Rev_OptRev/OptCF_hist,rt,f,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.002688 1.003360 1.016450 1.003037 1.012454 1.024681 1.053300 1.130232 1.033844 1.014690 1.008818 1.003079 1.006100 1.009235 1.007866 NaN 1.000499 1.002215 1.001037 1.002254 1.005186 1.006589 1.006313 1.002142 1.002381 1.001636 1.000513 1.001315 1.003168 1.002295 1.003754 1.003235 1.002046 1.004041 1.004510 1.002122 1.005287 1.007832 1.005371 1.003344 1.005915 1.002871 1.002802 1.000010 1.000881 1.002822 1.002938 1.002728 1.044920 1.122097
std 0.002277 0.003423 0.029898 0.002270 0.004969 0.015200 0.021244 0.039487 0.007243 0.008238 0.004198 0.002073 0.003525 0.005089 0.004615 NaN 0.000466 0.001075 0.000878 0.001970 0.001718 0.002301 0.001543 0.001463 0.001413 0.001301 0.001024 0.003461 0.005078 0.002436 0.002915 0.002254 0.001557 0.003722 0.003256 0.002319 0.004017 0.007522 0.004157 0.001387 0.002255 0.001351 0.003113 0.000793 0.000891 0.001644 0.001760 0.001688 0.030577 0.077523
min 0.998308 0.998994 0.997390 0.999009 1.001471 0.999447 1.013205 1.032940 1.004131 1.002671 1.000497 0.993792 0.992758 0.999948 0.997546 NaN 0.999817 1.000013 0.997450 0.995219 1.001508 1.001294 0.999804 0.999461 0.999000 0.998991 0.999453 0.998649 0.998124 0.998754 0.999393 1.000035 0.998343 0.998689 0.999806 0.996553 0.998025 0.998131 1.000040 0.998348 1.000146 0.999791 0.999666 0.995498 0.998118 0.999146 0.997328 0.998984 0.996998 0.993048
2.5% 0.999760 0.999770 1.000962 1.000056 1.005265 1.005476 1.024108 1.058584 1.018466 1.006874 1.002641 0.999879 1.002680 1.003665 1.001905 NaN 0.999924 1.000691 0.998724 0.998729 1.002264 1.003184 1.003646 0.999999 0.999868 0.999732 0.999655 0.999264 0.999640 0.999614 0.999799 1.000581 0.999723 0.999773 0.999999 0.998573 0.999990 0.999067 1.000695 1.001065 1.002433 1.000969 1.000358 0.998566 0.999636 1.000366 1.000684 0.999998 1.005168 1.004913
25% 1.001406 1.001234 1.005282 1.001578 1.009397 1.013718 1.038686 1.110689 1.029160 1.011317 1.007401 1.002084 1.004502 1.006430 1.005478 NaN 1.000173 1.001597 1.000601 1.001001 1.004043 1.004831 1.005338 1.001112 1.001332 1.000644 1.000048 1.000031 1.000179 1.000859 1.001812 1.001562 1.000972 1.000934 1.002257 1.000542 1.002501 1.002978 1.002136 1.002473 1.004378 1.002023 1.001019 0.999654 1.000340 1.001598 1.001845 1.001378 1.014596 1.042818
50% 1.002519 1.002440 1.007272 1.002618 1.011600 1.020153 1.046734 1.128227 1.034713 1.013150 1.008552 1.002867 1.005624 1.008081 1.006908 NaN 1.000401 1.002076 1.000911 1.001802 1.005136 1.006293 1.006121 1.002033 1.002398 1.001401 1.000352 1.000302 1.001028 1.001751 1.003103 1.002441 1.001928 1.003314 1.003391 1.000994 1.003692 1.004609 1.004494 1.003203 1.005683 1.002665 1.001985 0.999981 1.000788 1.002542 1.002649 1.002752 1.034590 1.160020
75% 1.003555 1.004238 1.010560 1.003988 1.014891 1.032562 1.065843 1.146930 1.038690 1.014868 1.009712 1.003852 1.006750 1.010144 1.008694 NaN 1.000692 1.002603 1.001488 1.003443 1.006125 1.008043 1.007150 1.002930 1.003543 1.002180 1.000643 1.000782 1.004753 1.002967 1.004878 1.004151 1.002597 1.005146 1.006377 1.003911 1.008984 1.009309 1.006155 1.004034 1.007213 1.003422 1.003323 1.000272 1.001230 1.003733 1.003642 1.003643 1.073992 1.185047
97.5% 1.006609 1.013850 1.126914 1.008574 1.022666 1.056382 1.099080 1.202694 1.046780 1.035964 1.016889 1.007617 1.014883 1.025347 1.021794 NaN 1.001540 1.006213 1.003091 1.006272 1.008733 1.011876 1.009868 1.005508 1.005001 1.004264 1.001761 1.015269 1.016112 1.008465 1.011534 1.007728 1.006544 1.013541 1.012792 1.006570 1.014854 1.026446 1.016883 1.006508 1.011363 1.005812 1.014042 1.002046 1.002981 1.006591 1.006800 1.006825 1.092552 1.226043
max 1.036135 1.024102 1.187379 1.016021 1.043214 1.106972 1.174392 1.414609 1.052069 1.089397 1.063175 1.028569 1.060230 1.044713 1.035030 NaN 1.002406 1.008574 1.003808 1.007982 1.010324 1.014900 1.014126 1.007889 1.005891 1.007198 1.008644 1.019880 1.031035 1.014916 1.013034 1.008830 1.007643 1.015180 1.014256 1.008567 1.016377 1.029832 1.018715 1.011587 1.018701 1.018793 1.021508 1.004961 1.013788 1.012171 1.033491 1.009446 1.120918 1.310973
Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.001415 1.002330 1.007064 1.000855 1.004452 1.006482 1.012630 1.053642 1.051509 1.025275 1.008982 1.007456 1.023552 1.010702 1.008680 1.001697 1.000690 1.002039 1.002566 1.003711 1.003099 1.002323 1.004324 1.002925 1.002536 1.004237 1.002429 1.002158 1.003548 1.002964 1.004263 1.002021 1.001690 1.003222 1.001684 1.001882 1.002096 1.003229 1.002337 1.003567 1.004073 1.002340 1.002476 1.000266 1.001081 1.002465 1.001630 1.003009 1.009095 1.034967
std 0.001027 0.002871 0.006460 0.001174 0.003177 0.007640 0.006799 0.033171 0.009838 0.014526 0.004212 0.003820 0.003051 0.005241 0.005074 0.000648 0.000362 0.000910 0.000978 0.001836 0.001659 0.001251 0.006241 0.002824 0.001435 0.010374 0.011252 0.005012 0.006355 0.002488 0.003873 0.001370 0.001625 0.003682 0.001536 0.002154 0.001342 0.002594 0.001205 0.003440 0.001916 0.001854 0.001466 0.001148 0.002728 0.001655 0.001117 0.001537 0.004157 0.014447
min 0.999341 0.999243 0.999689 0.998285 0.998932 0.997738 0.996916 0.998301 1.012164 1.005186 1.003383 1.000202 1.013966 1.004242 1.001092 1.000307 0.999594 1.000015 0.998162 0.996743 0.998385 0.999769 1.001323 0.999403 0.999115 0.999094 0.999827 0.997854 0.999172 0.999574 0.999325 1.000054 0.999638 0.999541 0.999759 0.997136 0.999879 0.999766 1.000064 1.000039 0.999915 0.999678 0.999303 0.995723 0.997732 0.999648 0.999461 0.998515 0.997639 0.999390
2.5% 0.999826 0.999906 1.001843 0.999345 1.000990 0.999650 1.002850 1.010603 1.035433 1.015066 1.005142 1.004105 1.018189 1.005929 1.004003 1.000505 0.999929 1.000419 0.999268 1.000220 1.000880 1.000607 1.002390 1.000107 1.000011 1.000102 0.999864 0.999528 0.999956 1.000053 0.999600 1.000227 0.999835 1.000058 0.999947 0.998366 1.000231 1.000214 1.000630 1.001025 1.001726 1.000594 1.000551 0.999029 0.999273 1.000458 1.000267 1.000517 1.001328 1.009892
25% 1.000717 1.001138 1.004035 1.000221 1.002594 1.002809 1.008826 1.041737 1.047873 1.022309 1.007484 1.005961 1.021498 1.008543 1.006188 1.001236 1.000531 1.001547 1.002142 1.002655 1.002046 1.001639 1.003414 1.001740 1.001595 1.001640 1.000184 1.000013 1.000872 1.001570 1.002024 1.000954 1.000534 1.000857 1.000544 1.000443 1.000852 1.001319 1.001200 1.002550 1.003144 1.001455 1.001611 0.999815 1.000351 1.001398 1.001031 1.002091 1.006767 1.025974
50% 1.001283 1.001747 1.005423 1.000655 1.003891 1.004822 1.011377 1.051820 1.051538 1.024493 1.008450 1.006913 1.023361 1.009805 1.007433 1.001588 1.000665 1.001983 1.002590 1.003339 1.002846 1.002196 1.003940 1.002617 1.002526 1.002849 1.000540 1.000545 1.001982 1.002493 1.003329 1.001635 1.001098 1.001913 1.001414 1.001075 1.001864 1.002609 1.002299 1.003387 1.004008 1.002043 1.002271 1.000059 1.000953 1.002255 1.001442 1.002896 1.008861 1.031005
75% 1.001979 1.002473 1.007019 1.001233 1.005477 1.007494 1.014976 1.060932 1.054878 1.026484 1.009337 1.008087 1.025315 1.011196 1.008995 1.002116 1.000897 1.002446 1.003103 1.004712 1.003709 1.002832 1.004579 1.003680 1.003378 1.004126 1.001028 1.001573 1.004802 1.003625 1.005394 1.002836 1.002327 1.002693 1.002113 1.003592 1.003151 1.003601 1.003337 1.004155 1.004775 1.002831 1.003206 1.000403 1.001461 1.003213 1.002012 1.003685 1.011118 1.043570
97.5% 1.003483 1.010954 1.026204 1.003323 1.014081 1.026925 1.030293 1.110678 1.063882 1.041061 1.020644 1.014280 1.029847 1.022904 1.020938 1.002875 1.001463 1.004832 1.004301 1.007505 1.006232 1.004624 1.006169 1.006507 1.004767 1.007940 1.012093 1.014245 1.010807 1.008263 1.011443 1.004957 1.005996 1.012651 1.005368 1.006210 1.005276 1.009954 1.004332 1.006397 1.006837 1.005334 1.004731 1.003178 1.002950 1.005714 1.003946 1.006160 1.018514 1.067021
max 1.009463 1.028702 1.071279 1.021894 1.027648 1.069368 1.076988 1.559459 1.242170 1.514284 1.060958 1.073666 1.044821 1.097889 1.082642 1.003642 1.002037 1.007462 1.005979 1.009941 1.014037 1.024954 1.192813 1.034138 1.010022 1.107741 1.106700 1.065326 1.072182 1.023332 1.037185 1.005632 1.006721 1.014103 1.006411 1.007347 1.005598 1.012860 1.005270 1.092097 1.056405 1.042595 1.034658 1.030248 1.140447 1.038580 1.017851 1.012461 1.031381 1.086942
Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.006924 1.017260 1.034871 1.016763 1.038748 1.102288 1.124199 1.224193 1.038122 1.018380 1.010248 1.004491 1.008869 1.012915 1.014924 NaN 1.000705 1.002337 1.001078 1.002446 1.007051 1.017867 1.014087 1.009200 1.009378 1.013954 1.014917 1.010220 1.011809 1.009344 1.010108 1.008013 1.003729 1.005993 1.007946 1.004165 1.008800 1.025230 1.018216 1.005897 1.007085 1.003903 1.003440 1.001905 1.004350 1.005058 1.005149 1.069101 1.141030 1.293522
std 0.013094 0.017952 0.038904 0.011475 0.017882 0.079572 0.048512 0.101965 0.009986 0.011168 0.006423 0.005160 0.007187 0.008909 0.017603 NaN 0.001537 0.001295 0.000947 0.002073 0.001685 0.006325 0.022729 0.012343 0.010635 0.034744 0.052587 0.031915 0.030239 0.014901 0.019770 0.003998 0.002317 0.004757 0.009397 0.006032 0.008577 0.046895 0.039222 0.011426 0.004580 0.005516 0.004525 0.006497 0.010279 0.008630 0.004985 0.013412 0.039766 0.181766
min 1.001017 1.004788 1.005489 1.005814 1.014471 1.021935 1.056934 1.105384 1.016020 1.007096 1.001471 0.993890 0.993313 1.001296 0.999223 NaN 0.999817 1.000013 0.997681 0.995390 1.002972 1.009822 1.005241 0.999758 1.001350 1.000271 0.999660 0.998656 0.998125 0.999219 0.999405 1.001628 0.998592 1.000520 1.001230 0.997584 0.999135 1.000643 1.002145 0.998591 1.001100 0.999806 0.999738 0.995537 0.998783 0.999160 0.999699 1.046200 1.073849 1.042460
2.5% 1.002245 1.005830 1.008270 1.007605 1.020739 1.029307 1.071240 1.129142 1.023062 1.009727 1.005054 1.000093 1.004027 1.005346 1.004812 NaN 0.999943 1.000731 0.998724 0.998819 1.004176 1.012086 1.006967 1.000747 1.002071 1.000888 0.999731 0.999686 0.999751 1.000618 1.000469 1.002098 1.001309 1.001405 1.001684 0.999992 1.002212 1.001331 1.002502 1.001667 1.002768 1.001088 1.000462 0.999009 1.000119 1.000642 1.001225 1.052142 1.084552 1.057540
25% 1.003977 1.008302 1.013275 1.009732 1.025042 1.036476 1.085945 1.168903 1.032350 1.012968 1.007979 1.002452 1.005543 1.008086 1.007334 NaN 1.000180 1.001685 1.000601 1.001110 1.005886 1.014926 1.008869 1.002219 1.003771 1.002394 1.000582 1.001020 1.001145 1.002683 1.003374 1.005357 1.002263 1.002866 1.003290 1.000911 1.003975 1.004517 1.005265 1.003245 1.005293 1.002282 1.001192 0.999993 1.001174 1.001934 1.002331 1.057625 1.104250 1.130105
50% 1.005564 1.011347 1.017422 1.013285 1.030792 1.046212 1.103048 1.196672 1.037183 1.014763 1.009234 1.003326 1.006872 1.010248 1.009585 NaN 1.000432 1.002134 1.000928 1.001974 1.006976 1.016362 1.009864 1.003906 1.005653 1.005236 1.002497 1.002085 1.005214 1.004832 1.005275 1.007934 1.002891 1.004770 1.006027 1.002304 1.006242 1.009081 1.006725 1.004289 1.006597 1.002963 1.002251 1.000538 1.002160 1.003270 1.003223 1.066360 1.135252 1.284267
75% 1.006701 1.018720 1.033033 1.021152 1.052427 1.176875 1.159690 1.244334 1.041997 1.018074 1.010599 1.004768 1.009041 1.014142 1.016854 NaN 1.000813 1.002690 1.001504 1.004084 1.007973 1.018499 1.011581 1.012797 1.010137 1.013003 1.007155 1.008117 1.013143 1.008851 1.010464 1.009530 1.004776 1.007065 1.008305 1.005438 1.010348 1.028101 1.012841 1.005768 1.007867 1.003978 1.003695 1.001786 1.003426 1.005336 1.007029 1.079461 1.178069 1.474338
97.5% 1.022447 1.073989 1.159545 1.046390 1.079502 1.269531 1.220001 1.420512 1.059230 1.050340 1.025488 1.017158 1.029729 1.037032 1.045928 NaN 1.002368 1.006301 1.003595 1.006475 1.010649 1.033241 1.071368 1.034275 1.045548 1.078651 1.084527 1.086278 1.050446 1.057830 1.041150 1.016551 1.008806 1.022892 1.052507 1.028357 1.041081 1.223036 1.197026 1.020585 1.015497 1.014313 1.015958 1.011869 1.023217 1.021189 1.016518 1.098921 1.209930 1.638419
max 1.291673 1.131301 1.253264 1.082055 1.166288 1.338491 1.443184 2.159810 1.138445 1.114905 1.115892 1.059748 1.068929 1.125101 1.347413 NaN 1.016929 1.016805 1.004362 1.008139 1.011737 1.074771 1.467675 1.092822 1.066993 1.371152 1.466804 1.420097 1.334974 1.126758 1.247010 1.033603 1.016984 1.028847 1.063301 1.053436 1.085185 1.261031 1.216046 1.460574 1.113820 1.110387 1.072655 1.150027 1.266746 1.156913 1.093964 1.143931 1.342019 2.087167

Results synthesis for CAISO 2017

In [43]:
### Rename labels
def labelfy(datum):
    out = ''
    
    if '_f' in datum:
        out += 'Fixed\n'
    else:
        out += 'Track\n'
    
    if ('optrev' in datum) and ('_f' in datum):
        out += 'Rev. opt.\n'
    elif '_f' in datum:
        out += 'CF opt.\n'
    else:
        out += 'Default\n'
    
    if '(curtail)' in datum:
        out += 'Curtail'
    else:
        out += 'Must-run'
    
    return out

columnorder = [
    'Fixed\nCF opt.\nCurtail', 
    'Fixed\nRev. opt.\nMust-run',
    'Fixed\nRev. opt.\nCurtail', 
    'Track\nDefault\nMust-run',
    'Track\nDefault\nCurtail',
]

########## Absolute values
iso = 'CAISO'
year = 2017

squeeze = 0.3
ncols = 2
gridspec_kw = {'width_ratios': [2, 2], 'wspace': 0.1}
figsize = (12,4)
dpi = None

###### Column 0: Historical, baseline
datum = 'Revenue'
markets = ['da','rt']
data_baseline = [
    ### Column 0, vs years
    'Revenue_hist_optcf_f(da)(mustrun)',
    'Revenue_hist_optcf_f(rt)(mustrun)',
]

### Data-indexed parameters
ylim = [0,145]
ylabel = 'Revenue [$/kWac-yr]'

###### Column 1: single year, TOS
data_tos = [
    ### Column 1, single year, row 0
    'Revenue_dispatched_hist_optcf_f(da)(curtail)',
    'Revenue_dispatched_hist_optcf_f(rt)(curtail)',
    'Revenue_hist_optrev_f(da)(mustrun)',
    'Revenue_hist_optrev_f(rt)(mustrun)',
    'Revenue_dispatched_hist_optrev_f(da)(curtail)',
    'Revenue_dispatched_hist_optrev_f(rt)(curtail)',
    'Revenue_hist_default_t(da)(mustrun)',
    'Revenue_hist_default_t(rt)(mustrun)',
    'Revenue_hist_default_t(da)(curtail)',
    'Revenue_hist_default_t(rt)(curtail)',
]
dfright = pd.melt(
    dfplot.loc[(dfplot.ISOwecc==iso)&(dfplot.yearlmp==year),['ISO:Node']+data_tos],
    id_vars=['ISO:Node']
)
dfright['market'] = dfright.variable.map(lambda x: 'da' if '(da)' in x else 'rt')
dfright['label'] = dfright.variable.map(labelfy)
dfright['datum'] = dfright.variable.map(lambda x: x.split('_')[0])

### Plot it
plt.close()
f,ax = plt.subplots(1,ncols,sharex='col',sharey='row', gridspec_kw=gridspec_kw, 
                    figsize=figsize, dpi=dpi,
                   )
### Column 0: Baseline data vs years (fixed, CFopt, must-run)
medians = []
for column in data_baseline:
    dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
               .pivot(index='ISO:Node',columns='yearlmp',values=column))

    pvvm.plots.plotquarthist(
        ax=ax[0], dfplot=dfframe, bootstrap=bootstrap, density=True,
        hist_range=ylim,
        histcolor=(mc['da'] if '(da)' in column else mc['rt']), 
        direction=('left' if '(da)' in column else 'right'), 
        squeeze=squeeze,
        quartpad=(-0.1 if '(da)' in column else 0.1),
        histpad=(-0.15 if '(da)' in column else 0.15),
        # medianmarker='_', mediansize=10, medianfacecolor='k'
    )
### Column 1: Single-year data vs TOS strategy
for market in markets:
    dfframe = dfright.loc[
        (dfright.market==market)&(dfright.datum==datum)
    ].pivot(index='ISO:Node',columns='label',values='value')[columnorder]
    medians.append(dfframe[columnorder[-1]].median())

    pvvm.plots.plotquarthist(
        ax=ax[1], dfplot=dfframe, bootstrap=bootstrap, density=True,
        x_locations=range(dfframe.shape[1]),
        hist_range=ylim,
        histcolor=mc[market], 
        direction=('left' if market == 'da' else 'right'), 
        squeeze=squeeze,
        quartpad=(-0.06 if market == 'da' else 0.06),
        histpad=(-0.11 if market == 'da' else 0.11),
        # medianmarker='_', mediansize=10, medianfacecolor='k'
    )

### Format axis
### Left
ax[0].set_ylabel(ylabel, weight='bold')
ax[0].set_xlim(2009.4,2018)
ax[0].set_xticks([2010,2012,2014,2016])
ax[0].set_xticklabels(['2010','2012','2014','2016'], rotation=0, ha='center')
ax[0].xaxis.set_minor_locator(AutoMinorLocator(2))
ax[0].set_ylim(0)
### Right
ax[1].set_xlim(-0.6,4.6)

### Grid lines
for i in range(2):
#     ax[i].grid(which='major', axis='y', zorder=-1, lw=0.5, ls=(0,(3,6)))
    ax[i].grid(which='major', axis='y', zorder=-1, lw=0.25)
    
### Add title
ax[0].set_title('Baseline: Fixed, CF opt., must-run', weight='bold', size='x-large')
ax[1].set_title('2017', weight='bold', size='x-large')

pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[0].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7)

plt.show()
In [44]:
for column in data_tos:
    print(column)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[column].median().unstack('ISOwecc'))
Revenue_dispatched_hist_optcf_f(da)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 91.642956 NaN 105.497863 77.502471 112.758798 91.734709 NaN
2011 86.127027 163.342554 94.790453 69.637654 102.834552 89.623317 NaN
2012 76.242205 82.804797 75.495416 60.435458 79.442607 70.136512 NaN
2013 107.023902 79.332946 109.051458 61.371157 102.792563 72.538165 NaN
2014 108.889073 88.995977 119.572237 76.502404 113.466331 87.085898 NaN
2015 73.412060 67.027475 81.934850 55.225698 79.654977 67.237333 60.160760
2016 59.386822 59.710989 61.372094 55.933880 61.697934 57.702296 50.694685
2017 59.750699 58.689548 60.681659 57.310864 60.449408 57.116127 51.386261
Revenue_dispatched_hist_optcf_f(rt)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 94.591012 NaN NaN 74.748461 119.518094 91.013086 NaN
2011 81.541409 147.651843 95.315083 67.586972 102.904595 90.658277 NaN
2012 77.769232 70.934357 75.994444 60.223217 81.283416 71.710441 NaN
2013 100.083051 74.627151 108.962743 60.506492 103.764344 73.816333 NaN
2014 102.343692 81.102552 111.813865 73.019874 108.493946 87.620288 NaN
2015 74.092169 54.074342 77.620474 54.325588 76.626139 64.543321 47.904429
2016 59.067273 55.466713 58.401227 55.127363 59.708533 57.139688 43.235054
2017 58.849745 55.630056 57.996400 57.961796 57.311387 57.362941 50.198177
Revenue_hist_optrev_f(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 91.780130 NaN 105.663158 77.839414 112.975635 92.040608 NaN
2011 86.265822 171.663151 94.886344 69.931324 103.184464 89.915369 NaN
2012 76.709604 84.879363 75.649929 60.659546 79.641184 70.261575 NaN
2013 107.137153 79.962283 109.387344 61.384973 102.860512 72.659026 NaN
2014 109.336825 89.566856 119.955737 76.380780 113.653407 87.089907 NaN
2015 73.490282 68.630289 82.175311 55.308050 79.783423 67.245947 60.315465
2016 59.958390 60.237333 61.515509 56.104792 61.752049 57.813635 51.169277
2017 61.861851 59.110446 60.935487 57.415140 60.635622 57.190056 51.403162
Revenue_hist_optrev_f(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 94.457796 NaN NaN 74.741102 117.636972 91.053349 NaN
2011 80.960072 152.318357 95.362220 67.565627 103.318216 90.694282 NaN
2012 77.364859 71.723508 76.173223 60.026348 81.424795 71.890506 NaN
2013 98.895664 75.228430 108.980108 60.332796 104.213755 73.925827 NaN
2014 101.134964 81.256712 111.983595 72.876636 108.828758 87.539732 NaN
2015 71.753935 54.244745 77.852748 54.124228 76.857902 64.438592 45.296315
2016 58.082556 55.733125 58.163408 54.740153 59.704050 57.255894 41.435422
2017 60.152692 55.827446 58.063913 57.723777 56.878965 57.451400 43.670894
Revenue_dispatched_hist_optrev_f(da)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 91.789179 NaN 105.663158 77.839414 112.975635 92.040608 NaN
2011 86.268224 171.673538 94.886344 69.931324 103.184464 89.961304 NaN
2012 76.715458 84.879363 75.649929 60.661136 79.641184 70.261575 NaN
2013 107.137153 79.962283 109.387344 61.384973 102.860512 72.659026 NaN
2014 109.338862 89.597928 119.955737 76.531748 113.653407 87.089907 NaN
2015 73.586390 68.632155 82.175435 55.308727 79.783423 67.245947 60.315465
2016 60.015474 60.242900 61.515509 56.104792 61.752049 57.819155 51.169286
2017 62.298697 59.110446 60.935487 57.415140 60.635622 57.190056 52.088554
Revenue_dispatched_hist_optrev_f(rt)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 94.766971 NaN NaN 74.868690 119.748813 91.203533 NaN
2011 81.717005 152.839929 95.362220 67.705604 103.378367 91.077774 NaN
2012 78.568522 71.841250 76.173223 60.273039 81.444175 71.916389 NaN
2013 100.368017 75.278542 108.980108 60.526729 104.270211 73.941487 NaN
2014 103.310672 81.294423 111.998438 73.023562 108.864018 87.627690 NaN
2015 75.168976 54.400999 77.988920 54.333688 76.886210 64.577990 48.039159
2016 61.502654 55.926224 58.763805 55.277872 59.843490 57.309070 44.276462
2017 64.697122 56.012118 58.307083 58.139875 57.422286 57.523415 54.225021
Revenue_hist_default_t(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 109.37760 NaN 118.5928 90.77040 129.87750 106.89810 NaN
2011 102.10010 193.97660 104.4916 80.83840 114.73255 102.60605 NaN
2012 90.92750 97.70400 83.0162 70.27660 88.80570 80.90280 NaN
2013 126.72775 95.37130 116.2047 70.30390 112.78160 83.88555 NaN
2014 131.29445 107.04620 124.8249 87.30930 119.94860 97.96785 NaN
2015 88.43770 79.49995 88.4539 63.84860 86.88680 76.16690 72.9487
2016 73.72300 70.06105 67.8385 64.80445 68.98870 66.23390 63.1331
2017 76.93390 69.36600 65.3982 66.44190 68.53490 65.49140 66.4538
Revenue_hist_default_t(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 111.2517 NaN NaN 86.92090 132.97770 106.03490 NaN
2011 93.1784 173.00860 105.50010 77.71110 115.39015 104.07990 NaN
2012 92.6374 82.76530 84.50340 69.00330 91.31930 82.74450 NaN
2013 116.3315 89.29950 117.86840 69.03940 116.56040 84.98410 NaN
2014 123.2419 97.32225 116.99075 84.53320 113.77750 97.37860 NaN
2015 86.4133 63.83250 85.39750 62.22110 84.56890 72.98540 54.5495
2016 70.2491 64.79050 65.02210 63.61755 67.75440 65.74120 51.1222
2017 70.3169 65.61200 62.86090 66.82590 64.31565 65.82685 50.9204
Revenue_hist_default_t(da)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 109.38250 NaN 118.5928 90.77040 129.87750 106.89810 NaN
2011 102.11430 194.01830 104.4916 80.83840 114.73255 102.60605 NaN
2012 90.93025 97.70690 83.0162 70.27660 88.80570 80.91480 NaN
2013 126.72775 95.37130 116.2047 70.30390 112.78160 83.88555 NaN
2014 131.30400 107.06850 124.8249 87.31920 119.94860 97.96785 NaN
2015 88.48905 79.50125 88.4555 63.95790 86.88680 76.17050 72.9487
2016 73.80240 70.08585 67.8386 64.80445 68.98870 66.23390 63.1331
2017 77.45070 69.38910 65.3982 66.47765 68.53490 65.49140 67.1264
Revenue_hist_default_t(rt)(curtail)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 111.95470 NaN NaN 87.1449 135.25100 106.37000 NaN
2011 94.63030 173.41900 105.50010 77.9661 115.49405 104.23975 NaN
2012 94.52365 82.94470 84.50340 69.9133 91.36170 82.80290 NaN
2013 118.28190 89.36690 117.86840 69.1420 116.61170 85.04810 NaN
2014 126.15765 97.35920 116.99685 84.8838 113.88280 97.55180 NaN
2015 90.29275 63.96395 85.68730 62.6773 84.87550 73.22020 57.81550
2016 74.94720 64.97755 65.75870 64.0214 67.82570 65.78420 54.39795
2017 76.77800 65.92965 63.19395 67.0154 64.43620 65.90585 66.26690

Orientation optimization, 1-axis tracking

Orientation

In [36]:
########## Starting absolute values for track CF-opt
systemtype = 'track'
pricecutoff = 'no'
yearsun = 'tmy'
program = 'PVvalueOptV4'

### Data-indexed parameters
data = [
    'OptCF_Azimuth(track)',
    'OptRev_Azimuth(track)(da)(mustrun)',
    'OptRev_Azimuth(track)(rt)(mustrun)',
]
colindex = [0, 1, 1,]
colindex = dict(zip(data, colindex))
direction = ['right','left','right',]
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [[2011,2017],slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [90, 270],
    [90, 270],
]
xlim = [
    [2017, 2018.9],
    [2009.4, 2018],
]

majlocs = [45, 45,]
minlocs = [1, 1,]

ylabel = [
    'Azimuth',
    'Azimuth',
]

note = [
    'CF Opt.',
    'Revenue Opt. (must-run)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [0.2, 2,], 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=True, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=5, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Optimized orientation, 1-axis track', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
In [38]:
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
OptCF_Azimuth(track) OptRev_Azimuth(track)(da)(mustrun) OptRev_Azimuth(track)(rt)(mustrun)
count 2209.000000 2209.000000 2209.000000
mean 179.010462 174.596272 170.409754
std 1.725166 1.544257 1.929457
min 172.581300 169.397900 163.715300
2.5% 175.767100 171.660620 166.723000
25% 177.990600 173.524600 169.114200
50% 178.988600 174.628600 170.378100
75% 180.043100 175.732000 171.763000
97.5% 182.775400 177.459500 174.119160
max 186.961600 179.733500 176.149900
median
OptCF_Azimuth(track) OptRev_Azimuth(track)(da)(mustrun) OptRev_Azimuth(track)(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 178.97240 NaN 185.05600 179.54370 181.1328 180.41720 NaN 178.58595 NaN 186.91200 183.16280 182.34860 182.21985 NaN 179.13560 NaN NaN 183.29540 181.42395 183.09640 NaN
2011 178.97240 178.3847 184.96160 179.57210 181.1328 180.41390 NaN 178.83910 169.6138 187.68390 182.52890 181.41745 181.61540 NaN 179.34280 174.99495 189.56430 183.44170 181.99635 182.24200 NaN
2012 178.97240 178.3822 184.94860 179.60610 181.1328 180.34830 NaN 175.68760 173.1899 187.58490 180.40680 181.02490 180.94900 NaN 179.31285 174.91210 187.28660 181.27530 181.26600 181.82320 NaN
2013 178.97240 178.3822 184.87650 179.63755 181.1328 180.34185 NaN 178.96795 177.4774 196.27420 182.19165 184.39430 181.25070 NaN 180.64480 175.85250 197.92920 183.40790 184.72880 182.75880 NaN
2014 178.98235 178.3769 184.86325 179.20680 181.1328 180.31040 NaN 177.45800 177.6657 263.67295 181.44635 190.26280 186.44160 NaN 176.21910 179.44475 263.56410 183.20460 261.83265 190.60390 NaN
2015 178.98235 178.3858 184.97710 179.20680 181.1328 180.31020 181.61460 177.12110 173.7177 193.04880 178.29650 184.35460 182.45870 179.4361 177.56240 178.49825 187.58875 179.60560 181.22120 183.89120 183.4163
2016 178.98400 178.3858 185.08020 179.21950 181.1328 180.28070 181.97755 175.74770 173.6436 187.69580 176.85090 182.54630 180.33620 178.8080 169.46480 174.21235 185.44820 177.81440 183.79270 181.15270 173.5283
2017 178.98860 178.3869 185.09420 179.21950 181.1328 180.27610 181.15130 174.62860 175.4276 189.65600 177.52110 182.86015 180.85790 178.9640 170.37810 176.01455 188.01640 178.61555 182.55705 179.44275 175.4903
max
OptCF_Azimuth(track) OptRev_Azimuth(track)(da)(mustrun) OptRev_Azimuth(track)(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 186.9616 NaN 191.2858 184.8219 185.4557 185.7092 NaN 187.6270 NaN 194.2195 187.5372 187.3184 188.8358 NaN 265.3918 NaN NaN 189.8596 186.8429 196.5247 NaN
2011 186.9616 181.269 191.2858 184.8219 185.4557 185.7092 NaN 187.9552 176.9438 194.6961 185.6642 187.9583 186.4591 NaN 189.8977 182.1243 198.0822 187.2886 185.9526 186.9667 NaN
2012 186.9616 181.269 191.2858 184.8219 185.4557 185.7092 NaN 186.5741 181.2621 195.1537 184.3992 186.5211 187.9490 NaN 198.5139 183.2112 195.8348 188.5268 188.1196 188.5675 NaN
2013 186.9616 181.269 191.2858 184.8219 185.4557 185.7092 NaN 185.8293 179.5786 268.6562 187.1347 195.8164 188.0083 NaN 190.4561 184.2788 266.8571 191.5648 192.6819 195.7096 NaN
2014 186.9616 181.269 191.2858 184.8219 185.4557 185.7092 NaN 184.7099 182.9918 269.7753 191.0909 269.4239 268.5646 NaN 184.2766 189.8853 269.8379 191.9578 269.5888 264.1737 NaN
2015 186.9616 181.269 191.2858 184.8219 185.4557 185.7092 188.1054 182.6962 179.2040 269.6479 185.8616 193.8626 191.5178 184.9359 184.8021 194.2804 268.3690 188.4462 194.4349 202.9547 190.7999
2016 186.9616 181.269 191.2858 184.8219 185.4557 185.7092 188.1054 182.0394 179.1025 196.2937 183.0558 188.5322 185.9884 184.5777 180.6193 182.1753 196.3411 189.9954 192.3094 191.2423 193.9057
2017 186.9616 181.269 191.2858 184.8219 185.4557 185.7092 191.9505 179.7335 180.5620 262.4102 181.9695 190.6527 188.3331 184.3893 176.1499 190.8237 267.5094 184.1256 194.3232 203.0064 190.7729
OptCF_Azimuth(track)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.00000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 178.989961 178.983531 178.987637 178.998159 179.007179 179.007143 179.00766 179.010462 178.290023 178.287402 178.288748 178.286875 178.293928 178.293972 178.295346 184.756251 184.703406 184.690696 184.649794 184.648735 184.783086 184.858498 184.932275 179.055477 179.030391 179.041531 179.067105 179.046885 179.053782 179.080310 179.086934 180.994657 180.969683 181.002653 180.988701 180.976689 180.973595 180.967991 180.967991 180.429168 180.423942 180.344297 180.346795 180.321169 180.320242 180.299894 180.291709 181.234842 181.609208 180.996075
std 1.738383 1.747644 1.739322 1.729052 1.727049 1.726302 1.72609 1.725166 1.045779 1.046590 1.046597 1.045123 1.045572 1.045501 1.046537 2.245942 2.286690 2.233978 2.247046 2.237022 2.192585 2.166196 2.122314 2.577188 2.635250 2.650976 2.641757 2.079048 2.079573 2.047661 2.023391 1.696755 1.701677 1.700095 1.701083 1.703607 1.702866 1.704928 1.704928 1.978276 1.974949 2.024270 2.025007 2.010171 2.011730 2.015444 2.009901 2.571735 2.141247 2.541189
min 172.581300 172.581300 172.581300 172.581300 172.581300 172.581300 172.58130 172.581300 173.835700 173.835700 173.835700 173.835700 173.835700 173.835700 173.835700 178.294800 178.294800 178.294800 178.038400 178.038400 177.048300 177.048300 177.048300 169.918900 169.918900 169.918900 169.918900 169.918900 169.918900 169.918900 169.918900 175.790500 175.790500 175.790500 175.790500 175.790500 175.790500 175.790500 175.790500 172.703200 172.703200 172.703200 172.703200 172.703200 172.703200 172.703200 172.703200 171.399300 171.399300 168.207600
2.5% 175.733953 175.712400 175.712400 175.736705 175.746755 175.747425 175.74776 175.767100 176.205380 176.207345 176.207435 176.207480 176.207503 176.207503 176.207345 179.321800 179.017500 179.017500 179.203980 179.081603 179.594075 179.708180 179.727600 173.286115 172.092500 172.092500 172.092500 173.733010 173.739270 173.789350 173.836300 177.344980 177.354093 177.365102 177.370607 177.374277 177.375195 177.376113 177.376113 176.580080 176.608325 176.322355 176.306805 176.306400 176.308560 176.306400 176.306400 174.192000 175.383065 173.140000
25% 177.972600 177.972600 177.973900 177.988300 177.990600 177.990600 177.99060 177.990600 177.552900 177.552250 177.553700 177.555000 177.558900 177.558900 177.558900 183.558300 183.449300 183.449450 183.439900 183.487250 183.573500 183.648100 183.834600 177.465600 177.434600 177.434600 177.481100 178.185100 178.185100 178.197875 178.185100 180.065300 180.064375 180.065300 180.065300 180.062525 180.030800 180.000000 180.000000 179.011650 179.009100 178.895850 178.905200 178.902750 178.894300 178.881700 178.878650 180.271700 180.818000 180.533800
50% 178.972400 178.972400 178.972400 178.972400 178.982350 178.982350 178.98400 178.988600 178.384700 178.382200 178.382200 178.376900 178.385800 178.385800 178.386900 185.056000 184.961600 184.948600 184.876500 184.863250 184.977100 185.080200 185.094200 179.543700 179.572100 179.606100 179.637550 179.206800 179.206800 179.219500 179.219500 181.132800 181.132800 181.132800 181.132800 181.132800 181.132800 181.132800 181.132800 180.417200 180.413900 180.348300 180.341850 180.310400 180.310200 180.280700 180.276100 181.614600 181.977550 181.151300
75% 179.989300 179.987800 180.001725 180.026525 180.031000 180.031000 180.03100 180.043100 178.962300 178.961900 178.962300 178.961800 178.962450 178.962450 178.964600 186.385600 186.342100 186.252050 186.231000 186.232525 186.339525 186.407300 186.468600 180.617000 180.648600 180.648600 180.680525 180.411200 180.430600 180.379075 180.379075 181.606200 181.606200 181.818700 181.806975 181.806975 181.801650 181.796325 181.796325 181.891725 181.885550 181.820450 181.825000 181.780025 181.780000 181.757000 181.754950 182.842200 182.787200 182.355700
97.5% 182.775400 182.775400 182.775400 182.775400 182.775400 182.775400 182.77540 182.775400 180.338625 180.337300 180.359825 180.358500 180.357837 180.357837 180.362475 188.208320 188.204890 188.164665 188.204400 188.203933 188.206237 188.205870 188.204187 183.769915 183.614143 183.554230 183.518282 182.243185 182.237177 182.158737 182.197957 183.939800 183.939800 183.939800 183.939800 183.939800 183.939800 183.939800 183.939800 184.004700 184.004700 183.961185 183.956900 183.956900 183.956900 183.960450 183.953300 185.032980 184.966882 184.920900
max 186.961600 186.961600 186.961600 186.961600 186.961600 186.961600 186.96160 186.961600 181.269000 181.269000 181.269000 181.269000 181.269000 181.269000 181.269000 191.285800 191.285800 191.285800 191.285800 191.285800 191.285800 191.285800 191.285800 184.821900 184.821900 184.821900 184.821900 184.821900 184.821900 184.821900 184.821900 185.455700 185.455700 185.455700 185.455700 185.455700 185.455700 185.455700 185.455700 185.709200 185.709200 185.709200 185.709200 185.709200 185.709200 185.709200 185.709200 188.105400 188.105400 191.950500
OptRev_Azimuth(track)(da)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 178.713882 178.999812 175.495930 179.086554 177.576333 177.125808 175.782676 174.596272 169.861246 173.260760 177.362059 177.545527 173.838147 173.639293 175.331841 186.712780 187.473796 187.350811 216.386349 248.494249 195.225530 187.568029 189.867133 182.819345 182.112153 180.058041 181.940659 182.245056 178.841647 177.250035 177.735048 181.748337 181.697707 180.835985 183.688099 213.574147 183.623374 181.179863 182.001034 182.145177 181.663434 181.020327 181.241848 188.216710 182.392181 180.280785 180.872295 179.128759 178.312119 177.475891
std 1.864467 1.625216 2.310853 1.516823 1.455543 1.348115 1.513219 1.544257 1.851081 1.379359 1.055502 1.420970 1.029631 1.152666 1.362900 2.455959 2.331583 2.610775 33.350727 29.007532 13.556203 2.715838 6.143691 2.034358 1.799215 2.102332 2.205400 3.028569 2.332256 2.089512 1.827901 2.291019 1.924960 2.363182 2.890059 41.491710 4.494629 3.545547 2.379871 1.919041 1.689754 1.937177 1.966693 12.142939 2.511179 2.151055 2.293359 2.134882 2.187964 3.205738
min 172.377200 173.537100 167.257900 173.479600 172.471500 172.581300 170.985500 169.397900 166.111300 162.030200 169.723400 166.797500 170.108600 166.290700 162.741100 179.234900 180.594800 179.244600 178.922300 185.571400 174.972400 177.595300 177.231600 174.112100 174.379600 172.315200 172.734200 174.152200 171.479300 169.767200 169.731600 175.729400 177.312800 175.832200 176.321400 90.492300 162.131700 171.974900 175.974500 174.588300 175.820000 174.910600 174.909200 91.099800 174.450600 172.982500 173.475400 171.047900 167.008500 165.970900
2.5% 175.452193 176.070600 170.528108 176.401350 174.989812 174.562100 172.686900 171.660620 166.877345 171.218640 174.491000 175.078100 171.982600 171.486870 172.756500 181.086060 181.577020 181.054250 185.560600 192.989075 184.275700 180.528400 181.998712 178.804580 177.190517 174.183950 175.150390 177.999190 174.997825 174.207437 174.396980 177.154100 177.552400 176.607788 178.308657 176.671400 174.117000 172.462187 177.046525 178.085567 178.391087 177.170330 177.373933 179.768450 177.404480 175.835950 176.161300 173.417480 172.550465 169.475750
25% 177.615900 177.934100 174.019500 178.123000 176.585425 176.224950 174.849100 173.524600 168.498650 172.572550 176.882800 176.915900 173.205625 172.964100 174.528800 185.330100 186.373100 185.937800 192.758200 259.918325 190.492950 186.349500 187.501900 181.812050 181.070575 178.870800 180.989400 179.978300 177.168150 175.700975 176.502800 179.916500 180.273125 178.949950 181.739400 184.401600 181.802500 179.174975 180.498175 180.881675 180.489825 179.719100 179.958800 184.145700 180.686200 178.832700 179.353075 178.349200 177.362675 176.004600
50% 178.585950 178.839100 175.687600 178.967950 177.458000 177.121100 175.747700 174.628600 169.613800 173.189900 177.477400 177.665700 173.717700 173.643600 175.427600 186.912000 187.683900 187.584900 196.274200 263.672950 193.048800 187.695800 189.656000 183.162800 182.528900 180.406800 182.191650 181.446350 178.296500 176.850900 177.521100 182.348600 181.417450 181.024900 184.394300 190.262800 184.354600 182.546300 182.860150 182.219850 181.615400 180.949000 181.250700 186.441600 182.458700 180.336200 180.857900 179.436100 178.808000 178.964000
75% 179.845400 179.974300 177.087375 180.013300 178.463200 178.077500 176.727400 175.732000 171.213150 173.952700 178.004950 178.337700 174.379200 174.372150 176.179300 188.481200 189.040000 189.107350 263.185500 266.194550 195.922800 189.523000 191.105600 184.014750 183.336025 181.515700 183.189775 184.435875 180.788000 178.722200 179.203775 183.578500 183.258625 183.213100 185.124600 266.104900 186.592050 184.004100 183.448200 183.556700 182.852050 182.406750 182.500000 188.915800 183.970100 181.866800 182.557425 180.451300 179.553150 179.594400
97.5% 182.828800 182.244900 179.699373 182.370370 180.814328 179.761250 178.945280 177.459500 173.704110 175.837650 179.058010 179.727220 176.307325 175.741400 177.557005 190.927400 191.177080 191.838900 268.198500 268.159900 260.192500 191.683200 197.897250 185.934585 184.941475 183.834160 185.529800 188.241977 183.396160 181.000687 181.189900 185.721400 185.395627 184.621495 189.417835 267.917123 189.426795 185.131962 185.960913 185.541300 184.808300 184.439740 185.261342 201.673300 187.314660 184.245950 185.107700 182.508560 181.782800 181.743100
max 187.627000 187.955200 186.574100 185.829300 184.709900 182.696200 182.039400 179.733500 176.943800 181.262100 179.578600 182.991800 179.204000 179.102500 180.562000 194.219500 194.696100 195.153700 268.656200 269.775300 269.647900 196.293700 262.410200 187.537200 185.664200 184.399200 187.134700 191.090900 185.861600 183.055800 181.969500 187.318400 187.958300 186.521100 195.816400 269.423900 193.862600 188.532200 190.652700 188.835800 186.459100 187.949000 188.008300 268.564600 191.517800 185.988400 188.333100 184.935900 184.577700 184.389300
OptRev_Azimuth(track)(rt)(mustrun)
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 178.987086 179.472997 178.357221 180.696715 176.211363 177.466401 169.555050 170.409754 174.842097 174.804391 175.836472 179.446023 178.357593 174.047470 175.855528 NaN 189.569873 186.940394 204.275620 252.154475 188.184978 185.098017 187.865734 183.397075 183.211134 181.244416 183.454520 183.765346 179.696149 178.270592 178.538832 180.913046 181.808407 181.266061 184.044756 227.073927 180.026597 181.216105 181.502560 182.836921 182.224260 181.891185 182.269944 191.619911 183.987359 181.024038 179.342517 182.944008 174.477946 175.473331
std 4.698432 2.462558 5.345981 2.148960 1.664582 1.822996 2.964351 1.929457 2.738249 2.329700 1.605088 2.299832 1.905888 1.884440 2.147298 NaN 2.365967 2.748492 20.153979 25.958367 6.686715 3.245155 16.523640 2.216181 1.817621 2.378619 2.667391 2.982294 3.492462 2.939310 2.576068 2.474732 2.314364 2.150944 3.020389 42.139482 6.018570 6.907976 3.683422 2.229697 1.735962 2.240095 3.515749 8.648737 2.982964 2.359273 2.998628 2.743780 3.816782 2.726003
min 169.161100 171.600900 92.801100 174.122000 171.409400 169.861700 160.746700 163.715300 168.961600 152.150500 168.547600 161.409200 159.097500 155.914900 159.061300 NaN 183.835900 177.801200 181.957000 186.607800 170.906700 174.901700 90.519700 176.280400 177.458000 172.754000 172.408900 178.792900 169.383900 169.659200 167.140200 173.928400 175.620800 176.520100 176.705500 90.013200 158.546100 149.125300 167.626600 172.959100 175.977500 174.397600 166.842000 176.491000 165.357100 173.821000 167.169200 172.733600 162.674100 164.529000
2.5% 173.063925 174.261500 170.884233 176.856070 172.986885 173.622587 163.372390 166.723000 170.043588 170.575715 172.420375 174.994798 174.469310 170.452220 171.922375 NaN 184.790220 180.427090 187.736200 193.363288 179.178237 177.995400 175.949500 178.147870 179.493943 175.694660 176.035585 179.351195 172.967233 173.837175 173.216455 176.234488 177.442500 177.042597 177.864600 176.671400 166.960200 154.471600 170.885600 177.932797 178.898650 177.376175 170.500018 180.691425 178.335100 176.053050 173.427513 176.388260 168.326085 169.993200
25% 176.974500 178.058600 175.448300 179.056100 174.951975 176.301350 167.800900 169.114200 172.501150 173.928400 175.070850 178.530325 177.619925 173.357525 174.732975 NaN 188.008500 185.422200 195.529700 260.995000 184.963900 183.343500 185.847800 182.159500 181.990125 180.128700 182.198100 181.458800 176.825625 176.123700 177.201450 179.027900 180.461275 179.759800 182.225900 187.296800 178.094000 179.002275 179.434675 181.496100 181.005700 180.413950 181.073525 186.497800 182.136200 179.561600 177.317275 181.897300 171.742850 174.247300
50% 179.135600 179.342800 179.312850 180.644800 176.219100 177.562400 169.464800 170.378100 174.994950 174.912100 175.852500 179.444750 178.498250 174.212350 176.014550 NaN 189.564300 187.286600 197.929200 263.564100 187.588750 185.448200 188.016400 183.295400 183.441700 181.275300 183.407900 183.204600 179.605600 177.814400 178.615550 181.423950 181.996350 181.266000 184.728800 261.832650 181.221200 183.792700 182.557050 183.096400 182.242000 181.823200 182.758800 190.603900 183.891200 181.152700 179.442750 183.416300 173.528300 175.490300
75% 180.642800 180.759100 180.927375 182.156300 177.229900 178.809200 171.611800 171.763000 176.961225 175.945900 176.608950 180.440100 179.284725 174.989625 176.992925 NaN 191.276400 188.572400 201.531000 265.631250 190.710600 187.233000 189.341700 184.671200 184.509100 182.742300 185.000350 185.947425 182.525100 180.611350 180.596350 182.510700 183.428900 182.704800 185.909300 266.676200 184.295100 184.383000 183.888000 184.315875 183.498000 183.490750 184.152500 195.085150 185.772700 182.613800 181.416100 184.766300 176.552775 176.907200
97.5% 184.336480 184.788200 185.862000 185.015190 179.495805 180.614562 175.403470 174.119160 179.601650 178.446550 178.422840 183.716845 181.101075 176.917755 179.260375 NaN 193.672570 191.327160 263.644800 268.196653 196.810300 190.692140 201.980138 187.684730 186.225755 185.510260 189.242070 190.024285 185.570640 183.832825 183.067072 185.546992 185.117100 184.591700 189.135643 267.712900 189.264345 189.341000 186.116162 186.514900 185.422100 186.157835 187.473073 205.996887 190.315000 185.373650 184.163900 187.358260 183.940695 181.248000
max 265.391800 189.897700 198.513900 190.456100 184.276600 184.802100 180.619300 176.149900 182.124300 183.211200 184.278800 189.885300 194.280400 182.175300 190.823700 NaN 198.082200 195.834800 266.857100 269.837900 268.369000 196.341100 267.509400 189.859600 187.288600 188.526800 191.564800 191.957800 188.446200 189.995400 184.125600 186.842900 185.952600 188.119600 192.681900 269.588800 194.434900 192.309400 194.323200 196.524700 186.966700 188.567500 195.709600 264.173700 202.954700 191.242300 203.006400 190.799900 193.905700 190.772900

CF and revenue impacts

In [39]:
########## Starting absolute values for track CF-opt
### Data-indexed parameters
data = [
    'CF_track,optrev,da,mustrun/CF_track,default,da,mustrun',
    'CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun',
    'Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun',
    'Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun',
]
colindex = [0, 0, 1, 1,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [0.90, 1.04],
    [0.96,1.10],
]
xlim = [
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [0.05,0.05,0.05,0.05,]
minlocs = [2,2,2,2,]

ylabel = [
    'Capacity Factor',
    'Revenue',
]

note = [
    '(must-run)',
    '(must-run)',
#     '(curtailable)',
#     '(curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
        ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(0,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Revenue-opt. vs. default, 1-axis track, must-run', 
          weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
In [40]:
print('ISONE 2014')
display(dfplot.loc[(dfplot.ISOwecc=='ISONE')&(dfplot.yearlmp==2014),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
    print(datum)
    display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
ISONE 2014
CF_track,optrev,da,mustrun/CF_track,default,da,mustrun CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun
count 612.000000 612.000000 612.000000 612.000000
mean 0.950009 0.946429 1.006034 1.002358
std 0.026693 0.024231 0.007055 0.007336
min 0.918200 0.919893 0.974291 0.969552
2.5% 0.926324 0.926118 0.993690 0.987178
25% 0.932030 0.931881 1.002899 0.998801
50% 0.935889 0.935391 1.005401 1.003508
75% 0.952117 0.946606 1.008337 1.005644
97.5% 0.998058 0.997715 1.023781 1.016771
max 0.999892 0.999710 1.028671 1.023783
median
CF_track,optrev,da,mustrun/CF_track,default,da,mustrun CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 0.999985 NaN 0.999817 0.999743 1.000120 0.999957 NaN 0.999930 NaN NaN 0.999645 1.000018 0.999884 NaN 1.000017 NaN 1.000232 1.000038 1.000101 1.000011 NaN 1.000006 NaN NaN 1.000060 1.000019 1.000014 NaN
2011 1.000033 0.998260 0.999637 0.999896 1.000037 1.000037 NaN 0.999993 0.999827 0.999259 0.999819 1.000029 1.000008 NaN 1.000026 1.003243 1.000502 1.000039 1.000075 1.000030 NaN 1.000024 1.001344 1.000912 1.000052 1.000129 1.000005 NaN
2012 0.999685 0.999595 0.999169 1.000027 1.000018 0.999965 NaN 0.999886 0.999874 0.999282 0.999987 1.000080 0.999931 NaN 1.000277 1.001375 0.999896 1.000008 1.000069 1.000002 NaN 1.000042 1.001156 0.999907 1.000031 1.000046 0.999999 NaN
2013 1.000026 1.000004 0.997261 0.999882 0.999821 0.999979 NaN 0.999967 0.999841 0.996403 0.999726 0.999714 0.999879 NaN 1.000001 1.000107 1.002520 1.000059 1.000127 1.000009 NaN 1.000013 1.000766 1.004215 1.000103 0.999984 1.000041 NaN
2014 0.999940 1.000019 0.935889 0.999876 0.998162 0.999359 NaN 0.999779 0.999990 0.935391 0.999751 0.931320 0.998010 NaN 1.000070 1.000191 1.005401 1.000050 1.000367 1.001401 NaN 1.000377 1.000007 1.003508 1.000307 0.999673 1.004511 NaN
2015 0.999927 0.999478 0.996990 1.000003 0.999513 0.999850 0.999989 0.999944 1.000007 0.998985 0.999854 0.999666 0.999671 1.000085 1.000084 1.001089 0.999301 1.000033 0.999705 1.000088 1.000004 1.000120 1.000103 0.999214 1.000117 1.000067 1.000146 1.000271
2016 0.999552 0.999408 0.999358 0.999861 0.999744 0.999960 0.999864 0.997386 0.999543 0.999770 0.999854 0.999680 0.999920 0.998704 1.000000 1.000978 1.000389 1.000064 1.000005 0.999999 1.000002 1.001023 1.001062 0.999715 1.000022 1.000212 0.999957 1.000910
2017 0.999307 0.999938 0.998319 0.999977 0.999785 0.999920 0.999879 0.997834 0.999975 0.998821 0.999948 0.999661 0.999908 0.999192 1.000096 1.000686 1.000134 1.000089 0.999987 0.999992 1.000009 1.001412 1.000620 0.999193 1.000072 0.999839 0.999999 1.000655
max
CF_track,optrev,da,mustrun/CF_track,default,da,mustrun CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp
2010 1.001172 NaN 1.000792 1.000794 1.000564 1.000627 NaN 1.001016 NaN NaN 1.000706 1.000544 1.000627 NaN 1.001072 NaN 1.001377 1.001116 1.000920 1.000875 NaN 1.005547 NaN NaN 1.001298 1.000996 1.003363 NaN
2011 1.000752 1.000604 1.000835 1.000761 1.000658 1.000732 NaN 1.001044 1.000943 1.000672 1.000631 1.000683 1.000790 NaN 1.000690 1.004820 1.002973 1.000876 1.001074 1.000763 NaN 1.001565 1.003629 1.003724 1.000742 1.000928 1.001388 NaN
2012 1.001394 1.000677 1.000466 1.001388 1.000449 1.000473 NaN 1.000467 1.000692 1.000496 1.001374 1.000460 1.000475 NaN 1.003277 1.005793 1.001102 1.000707 1.000427 1.000577 NaN 1.007822 1.019852 1.000950 1.000996 1.000575 1.000831 NaN
2013 1.000664 1.000958 1.000032 1.001649 1.000309 1.000529 NaN 1.000619 1.000989 0.999847 1.001649 1.000311 1.000412 NaN 1.000678 1.002354 1.025779 1.001114 1.002037 1.000801 NaN 1.001768 1.003758 1.020453 1.003078 1.002317 1.003438 NaN
2014 1.000597 1.000674 0.999892 1.000269 1.000656 1.000427 NaN 1.000618 1.000593 0.999710 1.000188 1.000880 1.000436 NaN 1.000690 1.004980 1.028671 1.002310 1.018785 1.009572 NaN 1.001584 1.013000 1.023783 1.002918 1.028195 1.017517 NaN
2015 1.000596 1.000215 1.000164 1.000819 1.000443 1.000436 1.001821 1.000656 1.000478 1.000146 1.000565 1.000458 1.000375 1.001572 1.000787 1.002898 1.005466 1.000611 1.001643 1.001271 1.001794 1.002090 1.013555 1.002979 1.002814 1.004568 1.005158 1.001186
2016 1.000345 1.000269 1.000411 1.000922 1.000303 1.000406 1.000633 1.000005 1.000327 1.000582 1.000957 1.000316 1.000394 1.001466 1.000819 1.004871 1.001526 1.000957 1.001000 1.000468 1.001917 1.007918 1.013189 1.000683 1.001427 1.004024 1.001272 1.004588
2017 1.000641 1.000774 1.000206 1.001420 1.000371 1.000549 1.001046 1.000352 1.000698 1.000245 1.001325 1.000398 1.000490 1.001047 1.003549 1.007363 1.020839 1.001177 1.001083 1.000537 1.003041 1.006125 1.010200 1.013903 1.002069 1.002396 1.001522 1.005136
CF_track,optrev,da,mustrun/CF_track,default,da,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.999989 1.000036 0.999626 1.000035 0.999946 0.999904 0.999538 0.999243 0.998297 0.999530 0.999986 0.999996 0.999471 0.999381 0.999887 0.999752 0.999610 0.999057 0.980385 0.950009 0.994812 0.999229 0.997968 0.999670 0.999895 1.000032 0.999886 0.999701 0.999972 0.999830 0.999995 1.000044 1.000020 1.000012 0.999745 0.973314 0.999245 0.999795 0.999709 0.999946 1.000041 0.999963 0.999972 0.997467 0.999767 0.999948 0.999896 1.000021 0.999821 0.999800
std 0.000157 0.000164 0.000437 0.000135 0.000156 0.000213 0.000278 0.000488 0.000833 0.000461 0.000232 0.000267 0.000285 0.000368 0.000360 0.000504 0.000434 0.000792 0.025369 0.026693 0.009748 0.000657 0.003020 0.000372 0.000259 0.000239 0.000293 0.000534 0.000218 0.000321 0.000250 0.000194 0.000205 0.000213 0.000382 0.034834 0.000879 0.000231 0.000343 0.000221 0.000204 0.000181 0.000182 0.010511 0.000375 0.000191 0.000234 0.000264 0.000271 0.000393
min 0.999507 0.999010 0.996916 0.999611 0.999337 0.999000 0.998489 0.996503 0.996148 0.993974 0.997341 0.996037 0.997873 0.995912 0.993465 0.997061 0.998374 0.995640 0.932733 0.918200 0.934893 0.995072 0.958328 0.998621 0.999137 0.999346 0.999000 0.996473 0.999076 0.998959 0.999378 0.999145 0.998775 0.999181 0.996215 0.915434 0.994666 0.998922 0.998654 0.998169 0.999293 0.998734 0.999095 0.914935 0.995915 0.998803 0.997910 0.999129 0.997903 0.997729
2.5% 0.999701 0.999810 0.998631 0.999817 0.999666 0.999461 0.998916 0.998220 0.996782 0.998574 0.999406 0.999629 0.998932 0.998653 0.999233 0.998804 0.998800 0.997245 0.938519 0.926324 0.945590 0.997722 0.995170 0.998850 0.999373 0.999670 0.999423 0.998418 0.999553 0.999193 0.999570 0.999632 0.999646 0.999584 0.998940 0.925906 0.996719 0.999285 0.999203 0.999479 0.999649 0.999591 0.999586 0.992438 0.998745 0.999536 0.999301 0.999554 0.999160 0.998827
25% 0.999889 0.999940 0.999459 0.999935 0.999844 0.999774 0.999365 0.998920 0.997646 0.999354 0.999901 0.999916 0.999289 0.999196 0.999784 0.999488 0.999303 0.998592 0.948533 0.932030 0.995190 0.998882 0.997684 0.999457 0.999749 0.999914 0.999748 0.999596 0.999846 0.999626 0.999843 0.999924 0.999914 0.999882 0.999630 0.926795 0.998870 0.999701 0.999383 0.999813 0.999908 0.999849 0.999852 0.998607 0.999633 0.999837 0.999798 0.999878 0.999702 0.999648
50% 0.999985 1.000033 0.999685 1.000026 0.999940 0.999927 0.999552 0.999307 0.998260 0.999595 1.000004 1.000019 0.999478 0.999408 0.999938 0.999817 0.999637 0.999169 0.997261 0.935889 0.996990 0.999358 0.998319 0.999743 0.999896 1.000027 0.999882 0.999876 1.000003 0.999861 0.999977 1.000120 1.000037 1.000018 0.999821 0.998162 0.999513 0.999744 0.999785 0.999957 1.000037 0.999965 0.999979 0.999359 0.999850 0.999960 0.999920 0.999989 0.999864 0.999879
75% 1.000091 1.000126 0.999855 1.000119 1.000047 1.000046 0.999742 0.999574 0.999035 0.999780 1.000106 1.000125 0.999668 0.999642 1.000075 1.000058 0.999892 0.999749 0.998555 0.952117 0.997870 0.999700 0.998854 0.999938 1.000029 1.000116 1.000010 1.000043 1.000098 1.000044 1.000122 1.000153 1.000115 1.000199 0.999946 0.999880 0.999679 0.999972 0.999971 1.000095 1.000175 1.000094 1.000110 0.999796 1.000011 1.000084 1.000059 1.000109 0.999997 1.000012
97.5% 1.000288 1.000412 1.000368 1.000366 1.000258 1.000266 1.000001 1.000061 0.999684 1.000129 1.000316 1.000274 1.000013 0.999940 1.000288 1.000566 1.000387 1.000203 0.999769 0.998058 0.999299 1.000107 0.999878 1.000297 1.000468 1.000480 1.000260 1.000207 1.000395 1.000504 1.000520 1.000395 1.000439 1.000410 1.000230 1.000441 1.000208 1.000195 1.000227 1.000349 1.000461 1.000276 1.000276 1.000184 1.000221 1.000243 1.000231 1.000667 1.000228 1.000558
max 1.001172 1.000752 1.001394 1.000664 1.000597 1.000596 1.000345 1.000641 1.000604 1.000677 1.000958 1.000674 1.000215 1.000269 1.000774 1.000792 1.000835 1.000466 1.000032 0.999892 1.000164 1.000411 1.000206 1.000794 1.000761 1.001388 1.001649 1.000269 1.000819 1.000922 1.001420 1.000564 1.000658 1.000449 1.000309 1.000656 1.000443 1.000303 1.000371 1.000627 1.000732 1.000473 1.000529 1.000427 1.000436 1.000406 1.000549 1.001821 1.000633 1.001046
CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 0.999669 0.999969 0.999658 0.999953 0.999774 0.999888 0.997326 0.997816 0.999640 0.999729 0.999788 0.999919 0.999954 0.999424 0.999883 NaN 0.999211 0.999139 0.991287 0.946429 0.998033 0.999637 0.997008 0.999528 0.999729 0.999939 0.999669 0.999463 0.999770 0.999782 0.999905 0.999944 1.000030 1.000040 0.999696 0.960074 0.999365 0.999188 0.999598 0.999843 1.000005 0.999909 0.999756 0.996606 0.999490 0.999890 0.999855 1.000063 0.998634 0.999237
std 0.004982 0.000238 0.001726 0.000172 0.000225 0.000285 0.001135 0.000892 0.000542 0.000997 0.000341 0.000485 0.000463 0.000833 0.000565 NaN 0.000545 0.000777 0.014686 0.024231 0.005305 0.000565 0.008189 0.000545 0.000370 0.000324 0.000505 0.000685 0.000379 0.000370 0.000339 0.000266 0.000204 0.000193 0.000343 0.035951 0.000979 0.002388 0.000473 0.000321 0.000236 0.000228 0.000569 0.006479 0.000658 0.000240 0.000448 0.000301 0.001136 0.000722
min 0.884374 0.998256 0.960139 0.998812 0.998682 0.998280 0.993306 0.993499 0.997640 0.984333 0.997181 0.991247 0.990183 0.983468 0.990033 NaN 0.997678 0.995294 0.937190 0.919893 0.943020 0.995072 0.948890 0.997209 0.998377 0.998309 0.997021 0.995967 0.997686 0.997532 0.998459 0.998906 0.999256 0.999461 0.997692 0.914034 0.992289 0.985067 0.996839 0.997506 0.999044 0.998551 0.995776 0.916809 0.989978 0.997567 0.989016 0.998147 0.993883 0.994979
2.5% 0.999191 0.999447 0.998218 0.999620 0.999330 0.999152 0.994729 0.996035 0.998335 0.998386 0.998832 0.999062 0.999378 0.998364 0.998995 NaN 0.998111 0.997404 0.946293 0.926118 0.994446 0.998318 0.959299 0.998121 0.998851 0.999292 0.998643 0.997697 0.998885 0.999049 0.999090 0.999396 0.999560 0.999625 0.998871 0.919705 0.996363 0.989948 0.999008 0.999120 0.999508 0.999398 0.997752 0.988252 0.997862 0.999300 0.999096 0.999464 0.996151 0.997553
25% 0.999794 0.999879 0.999622 0.999857 0.999627 0.999764 0.996661 0.997235 0.999338 0.999671 0.999682 0.999877 0.999903 0.999271 0.999806 NaN 0.998849 0.998686 0.994612 0.931881 0.997925 0.999437 0.998163 0.999321 0.999526 0.999853 0.999500 0.999155 0.999611 0.999623 0.999800 0.999797 0.999925 0.999893 0.999567 0.926496 0.998962 0.999607 0.999231 0.999673 0.999864 0.999802 0.999697 0.995598 0.999267 0.999775 0.999769 0.999907 0.997750 0.998831
50% 0.999930 0.999993 0.999886 0.999967 0.999779 0.999944 0.997386 0.997834 0.999827 0.999874 0.999841 0.999990 1.000007 0.999543 0.999975 NaN 0.999259 0.999282 0.996403 0.935391 0.998985 0.999770 0.998821 0.999645 0.999819 0.999987 0.999726 0.999751 0.999854 0.999854 0.999948 1.000018 1.000029 1.000080 0.999714 0.931320 0.999666 0.999680 0.999661 0.999884 1.000008 0.999931 0.999879 0.998010 0.999671 0.999920 0.999908 1.000085 0.998704 0.999192
75% 1.000057 1.000097 1.000023 1.000059 0.999919 1.000073 0.998117 0.998486 1.000043 1.000032 0.999973 1.000101 1.000109 0.999745 1.000113 NaN 0.999535 0.999766 0.997252 0.946606 0.999514 0.999980 0.999288 0.999886 0.999975 1.000097 0.999924 0.999929 1.000022 1.000007 1.000118 1.000111 1.000192 1.000204 0.999913 0.999221 0.999974 0.999931 0.999952 1.000066 1.000164 1.000064 1.000038 0.999390 0.999915 1.000054 1.000052 1.000232 0.999684 0.999794
97.5% 1.000260 1.000355 1.000220 1.000309 1.000172 1.000274 0.999322 0.999333 1.000282 1.000275 1.000242 1.000240 1.000238 1.000079 1.000304 NaN 1.000187 1.000181 0.999339 0.997715 0.999997 1.000263 1.000035 1.000303 1.000283 1.000286 1.000228 1.000134 1.000201 1.000315 1.000278 1.000387 1.000440 1.000392 1.000257 1.000416 1.000235 1.000185 1.000222 1.000338 1.000455 1.000242 1.000257 1.000081 1.000193 1.000223 1.000227 1.000634 1.000218 1.000543
max 1.001016 1.001044 1.000467 1.000619 1.000618 1.000656 1.000005 1.000352 1.000943 1.000692 1.000989 1.000593 1.000478 1.000327 1.000698 NaN 1.000672 1.000496 0.999847 0.999710 1.000146 1.000582 1.000245 1.000706 1.000631 1.001374 1.001649 1.000188 1.000565 1.000957 1.001325 1.000544 1.000683 1.000460 1.000311 1.000880 1.000458 1.000316 1.000398 1.000627 1.000790 1.000475 1.000412 1.000436 1.000375 1.000394 1.000490 1.001572 1.001466 1.001047
Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2118.000000 2121.000000 2150.000000 2204.000000 2234.000000 2236.000000 2237.000000 2209.000000 1559.000000 1563.000000 1567.000000 1569.000000 1570.000000 1570.000000 1563.000000 409.000000 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.000039 1.000046 1.000427 1.000013 1.000105 1.000094 1.000028 1.000141 1.003246 1.001402 1.000148 1.000240 1.001117 1.001006 1.000764 1.000208 1.000573 0.999847 1.003006 1.006034 0.998664 1.000331 1.000162 1.000053 1.000062 1.000008 1.000089 1.000266 1.000025 1.000107 1.000145 1.000174 1.000128 1.000064 1.000197 0.996842 0.999694 1.000093 0.999952 1.000064 1.000094 1.000015 1.000008 1.001401 1.000087 0.999989 0.999967 1.000074 1.000062 1.000298
std 0.000122 0.000097 0.000486 0.000090 0.000134 0.000173 0.000217 0.000311 0.000742 0.000504 0.000203 0.000311 0.000307 0.000315 0.000451 0.000491 0.000434 0.000560 0.005256 0.007055 0.003073 0.000500 0.000914 0.000233 0.000152 0.000143 0.000228 0.000502 0.000165 0.000236 0.000240 0.000244 0.000206 0.000152 0.000365 0.008575 0.000608 0.000267 0.000260 0.000229 0.000195 0.000138 0.000109 0.002228 0.000274 0.000121 0.000148 0.000240 0.000234 0.000652
min 0.999513 0.999418 0.999624 0.999638 0.999775 0.999570 0.999124 0.999107 1.000480 1.000080 0.999857 0.999438 1.000299 1.000051 0.999852 0.998227 0.999381 0.997632 0.983600 0.974291 0.966893 0.998250 0.997615 0.999086 0.999489 0.999330 0.999120 0.998374 0.998852 0.999091 0.999455 0.999364 0.999577 0.999375 0.999314 0.965441 0.998025 0.999539 0.999433 0.998933 0.999193 0.999291 0.999298 0.973155 0.998083 0.999070 0.998782 0.999670 0.999298 0.999617
2.5% 0.999818 0.999917 0.999903 0.999861 0.999927 0.999798 0.999651 0.999705 1.001813 1.000647 0.999965 0.999998 1.000647 1.000513 1.000292 0.999214 0.999973 0.998596 0.990798 0.993690 0.988427 0.999126 0.998939 0.999651 0.999747 0.999691 0.999738 0.999345 0.999648 0.999680 0.999789 0.999823 0.999746 0.999746 0.999673 0.985855 0.998345 0.999771 0.999491 0.999658 0.999787 0.999723 0.999770 0.999291 0.999390 0.999687 0.999549 0.999855 0.999834 0.999863
25% 0.999985 0.999998 1.000075 0.999967 1.000011 0.999978 0.999894 0.999981 1.002699 1.001142 1.000060 1.000132 1.000905 1.000828 1.000534 0.999975 1.000305 0.999527 1.001058 1.002899 0.998044 1.000026 0.999787 0.999949 0.999977 0.999970 0.999980 0.999999 0.999974 0.999987 0.999997 0.999990 0.999995 0.999982 1.000025 0.987437 0.999323 0.999921 0.999830 0.999948 0.999984 0.999964 0.999966 1.000702 0.999981 0.999949 0.999928 0.999973 0.999982 0.999960
50% 1.000017 1.000026 1.000277 1.000001 1.000070 1.000084 1.000000 1.000096 1.003243 1.001375 1.000107 1.000191 1.001089 1.000978 1.000686 1.000232 1.000502 0.999896 1.002520 1.005401 0.999301 1.000389 1.000134 1.000038 1.000039 1.000008 1.000059 1.000050 1.000033 1.000064 1.000089 1.000101 1.000075 1.000069 1.000127 1.000367 0.999705 1.000005 0.999987 1.000011 1.000030 1.000002 1.000009 1.001401 1.000088 0.999999 0.999992 1.000004 1.000002 1.000009
75% 1.000078 1.000068 1.000619 1.000045 1.000178 1.000192 1.000120 1.000226 1.003915 1.001634 1.000186 1.000268 1.001269 1.001120 1.000881 1.000430 1.000811 1.000145 1.004620 1.008337 1.000236 1.000663 1.000464 1.000150 1.000133 1.000057 1.000176 1.000509 1.000101 1.000216 1.000264 1.000328 1.000260 1.000153 1.000297 1.002458 0.999994 1.000213 1.000078 1.000150 1.000175 1.000052 1.000055 1.002120 1.000250 1.000046 1.000032 1.000051 1.000064 1.000197
97.5% 1.000335 1.000313 1.001623 1.000211 1.000453 1.000475 1.000577 1.000955 1.004391 1.002098 1.000789 1.000603 1.001830 1.001869 1.001820 1.001142 1.001551 1.000819 1.017351 1.023781 1.001801 1.001202 1.001347 1.000496 1.000387 1.000328 1.000589 1.001550 1.000374 1.000576 1.000834 1.000619 1.000642 1.000401 1.001167 1.006759 1.001377 1.000860 1.000457 1.000640 1.000588 1.000368 1.000250 1.005196 1.000532 1.000217 1.000212 1.000729 1.000776 1.002262
max 1.001072 1.000690 1.003277 1.000678 1.000690 1.000787 1.000819 1.003549 1.004820 1.005793 1.002354 1.004980 1.002898 1.004871 1.007363 1.001377 1.002973 1.001102 1.025779 1.028671 1.005466 1.001526 1.020839 1.001116 1.000876 1.000707 1.001114 1.002310 1.000611 1.000957 1.001177 1.000920 1.001074 1.000427 1.002037 1.018785 1.001643 1.001000 1.001083 1.000875 1.000763 1.000577 1.000801 1.009572 1.001271 1.000468 1.000537 1.001794 1.001917 1.003041
Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun
ISOwecc CAISO ERCOT ISONE MISO NYISO PJM WECC -CAISO
yearlmp 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2015 2016 2017
count 2114.000000 2121.000000 2150.000000 2205.000000 2234.000000 2236.000000 2237.000000 2209.000000 1560.000000 1563.000000 1567.000000 1570.000000 1572.000000 1570.000000 1564.000000 0.0 437.000000 499.000000 593.000000 612.000000 746.000000 829.000000 966.000000 179.000000 192.000000 197.000000 200.000000 368.000000 370.000000 386.000000 378.000000 402.000000 412.000000 424.000000 430.000000 434.000000 435.000000 436.000000 436.000000 4288.000000 4366.000000 4967.000000 5044.000000 4936.000000 4857.000000 4741.000000 4686.000000 1049.000000 1824.000000 3461.000000
mean 1.000039 1.000059 1.000364 1.000049 1.000418 1.000170 1.001361 1.001523 1.001475 1.001200 1.000779 1.000030 1.000185 1.001243 1.000899 NaN 1.000937 0.999840 1.004304 1.002358 0.998684 0.999551 0.999056 1.000090 1.000097 1.000045 1.000150 1.000606 1.000175 1.000064 1.000166 1.000079 1.000195 1.000057 0.999995 0.999138 1.000129 1.000421 0.999808 1.000066 1.000024 0.999996 1.000134 1.004541 1.000158 0.999921 0.999990 1.000250 1.000867 1.000688
std 0.000290 0.000194 0.000767 0.000189 0.000277 0.000244 0.001318 0.000750 0.000964 0.001128 0.000418 0.000552 0.000648 0.000928 0.000912 NaN 0.000581 0.000530 0.003802 0.007336 0.002826 0.000667 0.001964 0.000319 0.000216 0.000265 0.000446 0.000692 0.000435 0.000272 0.000405 0.000275 0.000315 0.000136 0.000466 0.011324 0.000655 0.000882 0.000477 0.000305 0.000172 0.000180 0.000449 0.003077 0.000509 0.000231 0.000523 0.000247 0.000958 0.000635
min 0.997717 0.999441 0.993888 0.998633 0.999606 0.999220 0.996528 0.999546 0.999200 0.993535 0.998885 0.998074 0.995243 0.999125 0.995641 NaN 0.999329 0.997026 0.980139 0.969552 0.971525 0.994774 0.974714 0.998642 0.999564 0.998327 0.999256 0.999450 0.999097 0.998610 0.998632 0.999348 0.998963 0.999630 0.998927 0.949653 0.997813 0.997072 0.997280 0.998655 0.999037 0.998784 0.997573 0.978566 0.993104 0.998444 0.988573 0.998766 0.995179 0.996872
2.5% 0.999596 0.999762 0.999648 0.999746 0.999992 0.999772 0.999163 1.000470 0.999857 1.000036 1.000061 0.999577 0.999847 1.000420 1.000081 NaN 1.000027 0.998743 0.997771 0.987178 0.995138 0.998132 0.996833 0.999527 0.999721 0.999474 0.999441 0.999831 0.999625 0.999574 0.999523 0.999732 0.999620 0.999777 0.999387 0.970272 0.998647 0.998925 0.999048 0.999582 0.999690 0.999581 0.999620 0.999868 0.999211 0.999374 0.999344 0.999750 0.999090 0.999662
25% 0.999923 0.999968 0.999974 0.999962 1.000229 1.000014 1.000402 1.001010 1.000754 1.000926 1.000611 0.999960 1.000033 1.000886 1.000428 NaN 1.000567 0.999571 1.002966 0.998801 0.998340 0.999286 0.998607 0.999968 0.999947 0.999956 0.999963 1.000068 0.999990 0.999929 0.999971 0.999906 0.999996 0.999994 0.999576 0.995989 0.999851 0.999936 0.999559 0.999921 0.999943 0.999934 0.999962 1.002114 0.999978 0.999818 0.999853 1.000087 1.000046 1.000358
50% 1.000006 1.000024 1.000042 1.000013 1.000377 1.000120 1.001023 1.001412 1.001344 1.001156 1.000766 1.000007 1.000103 1.001062 1.000620 NaN 1.000912 0.999907 1.004215 1.003508 0.999214 0.999715 0.999193 1.000060 1.000052 1.000031 1.000103 1.000307 1.000117 1.000022 1.000072 1.000019 1.000129 1.000046 0.999984 0.999673 1.000067 1.000212 0.999839 1.000014 1.000005 0.999999 1.000041 1.004511 1.000146 0.999957 0.999999 1.000271 1.000910 1.000655
75% 1.000101 1.000106 1.000582 1.000108 1.000566 1.000285 1.002433 1.001901 1.002350 1.001392 1.000905 1.000051 1.000200 1.001317 1.001317 NaN 1.001290 1.000161 1.005822 1.005644 0.999848 0.999988 0.999729 1.000201 1.000238 1.000164 1.000266 1.001029 1.000271 1.000170 1.000256 1.000148 1.000358 1.000133 1.000264 1.005390 1.000413 1.001025 1.000014 1.000190 1.000101 1.000055 1.000171 1.006254 1.000328 1.000039 1.000144 1.000386 1.001664 1.000876
97.5% 1.000673 1.000592 1.002819 1.000583 1.001075 1.000695 1.003724 1.003406 1.003220 1.002498 1.001870 1.000482 1.000795 1.003005 1.002888 NaN 1.002173 1.000673 1.011365 1.016771 1.000410 1.000349 1.000815 1.000800 1.000620 1.000483 1.001543 1.002097 1.001843 1.000598 1.001256 1.000847 1.000768 1.000363 1.000950 1.015298 1.001381 1.002382 1.000938 1.000657 1.000433 1.000418 1.001533 1.010969 1.001232 1.000304 1.000745 1.000805 1.002332 1.002202
max 1.005547 1.001565 1.007822 1.001768 1.001584 1.002090 1.007918 1.006125 1.003629 1.019852 1.003758 1.013000 1.013555 1.013189 1.010200 NaN 1.003724 1.000950 1.020453 1.023783 1.002979 1.000683 1.013903 1.001298 1.000742 1.000996 1.003078 1.002918 1.002814 1.001427 1.002069 1.000996 1.000928 1.000575 1.002317 1.028195 1.004568 1.004024 1.002396 1.003363 1.001388 1.000831 1.003438 1.017517 1.005158 1.001272 1.001522 1.001186 1.004588 1.005136

Supplementary information

Geographic data

In [15]:
dfmap = dfplot.merge(
    dfin[['ISO:Node','yearlmp','Latitude','Longitude']].drop_duplicates(['ISO:Node','yearlmp']),
    on=['ISO:Node','yearlmp'], how='left'
).copy()
In [18]:
maplabel = {
    'OptRev_Azimuth(rt)(mustrun)': 'Azimuth [°]',
    'OptRev_Azimuth(rt)(curtail)': 'Azimuth [°]',
    'OptRev_Tilt(rt)(mustrun)': 'Tilt [°]',
    'OptRev_Tilt(rt)(curtail)': 'Tilt [°]',
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': 'Revenue ratio\ntrack/fixed',
    'Rev_curtail/Rev_mustrun(fixed)(rt)': 'Revenue ratio\ncurtail/must-run',
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': 'Revenue ratio\nRevenue-opt/CF-opt'
}

zrange = {
    'OptRev_Azimuth(rt)(mustrun)': [90,270],
    'OptRev_Tilt(rt)(mustrun)': [15,57],
    'OptRev_Azimuth(rt)(curtail)': [120,240],
    'OptRev_Tilt(rt)(curtail)': [15,47],
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': [1., 1.7],
    'Rev_curtail/Rev_mustrun(fixed)(rt)': [0.99,1.32],
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': [0.99,1.37],
}
zticks = {
    'OptRev_Azimuth(rt)(mustrun)': [90,135,180,225,270],
    'OptRev_Tilt(rt)(mustrun)': [20,30,40,50,],
    'OptRev_Azimuth(rt)(curtail)': [120,150,180,210,240],
    'OptRev_Tilt(rt)(curtail)': [15,25,35,45],
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': list(np.arange(1,1.71,0.1)),
    'Rev_curtail/Rev_mustrun(fixed)(rt)': [1.,1.1,1.2,1.3],
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': [1.,1.1,1.2,1.3]
}
cmap = plt.cm.coolwarm#{
#     'OptRev_Azimuth(rt)(mustrun)': plt.cm.coolwarm,
#     'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': plt.cm.coolwarm,
#     'Rev_curtail/Rev_mustrun(fixed)(rt)': plt.cm.coolwarm,
# }

data = [
#     'OptRev_Azimuth(rt)(mustrun)',
#     'OptRev_Tilt(rt)(mustrun)',
    'OptRev_Azimuth(rt)(curtail)',
    'OptRev_Tilt(rt)(curtail)',
    'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
    'Rev_curtail/Rev_mustrun(fixed)(rt)',
    'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
]
for datum in data:
    print('{}\n{}\n{}'.format('#'*30,datum,'#'*30))
    for year in range(2010,2018):
        plt.close()
        f,ax = pvvm.plots.plotusascattermap(
            dfmap.loc[dfmap.yearlmp==year], datum,
            zrange=zrange[datum],
            # colorbartitle=maplabel[datum],
            colorbarhist=False,
            cmap=cmap,
        )
        cbar, chist = pvvm.plots.addcolorbarhist(
            f=f, ax0=ax, data=dfmap.loc[dfmap.yearlmp==year,datum],
            title=maplabel[datum], cmap=cmap,
            vmin=zrange[datum][0], vmax=zrange[datum][1]
        )
        cbar.yaxis.set_ticks(zticks[datum])

        ax.set_title(year, y=0.93, fontsize='x-large', weight='bold')
        ax.annotate(
            datum.replace('/','\n/ '), 
            xy=(0.03,0.05), xycoords='axes fraction',
            ha='left', va='bottom', fontsize='medium')

        plt.show()
##############################
OptRev_Azimuth(rt)(curtail)
##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
##############################
OptRev_Tilt(rt)(curtail)
##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
##############################
Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
##############################
Rev_curtail/Rev_mustrun(fixed)(rt)
##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
##############################
Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: 
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  m_in.readshapefile(mappath, 'States', drawbounds=False)

Impact of temporal output shaping as differences instead of ratios

Curtailment

In [27]:
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CF_curtail-CF_mustrun(fixed)(da)',
    'CF_curtail-CF_mustrun(fixed)(rt)',
    'Rev_curtail-Rev_mustrun(fixed)(da)',
    'Rev_curtail-Rev_mustrun(fixed)(rt)',
]
colindex = [0, 0, 1, 1]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 2
ylim = [
    [-0.08, 0.01],
    [-1, 15],
]

ylabel = [
    'Capacity Factor',
    'Revenue [$/kWac-yr]',
]

note = [
    '',
    '',
]
y1 = 1     # 1.2 if using note
y2 = 1.04  # 1.07 if using note

gridspec_kw = {'width_ratios': [2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
#             format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
#         ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.1))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ax[(row,col)].axhline(0, lw=0.25, c='0.5')

ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, curtailable – must-run, fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

Tracking

In [30]:
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
    'CapacityFactor_track(def)-fixed(optcf)_hist,da,mustrun',
    'CapacityFactor_track(def)-fixed(optcf)_hist,da,curtail,baselinemustrun',
    'CapacityFactor_track(def)-fixed(optcf)_hist,rt,curtail,baselinemustrun',
    'Revenue_track(def)-fixed(optcf)_hist,da,mustrun',
    'Revenue_track(def)-fixed(optcf)_hist,rt,mustrun',
    'Revenue_track(def)-fixed(optcf)_hist,da,curtail,baselinemustrun',
    'Revenue_track(def)-fixed(optcf)_hist,rt,curtail,baselinemustrun',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))

### Column-indexed parameters
ncols = 4
ylim = [
    [-0.03,0.08],
    [-0.03,0.08],
    [-3,40],
    [-3,40],
]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue [$/kWac-yr]',
    'Revenue [$/kWac-yr]',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2   # 1.2 if using note, 1 if not
y2 = 1.07  # 1.07 if using note, 1.05 if not

gridspec_kw = {'width_ratios': [1, 2, 2, 2]}

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            # medianmarker='_', mediansize=10, medianfacecolor='k'
        )
#         ### NEW: Add revenue cutoff line
#         if datum.startswith('Revenue') and (',da,' in datum):
#             ax[(row,colindex[datum])].axhline(1.066, lw=0.25, ls=(0,(6,6)), c='0.5')

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ax[(row,col)].set_xlim(2009.4,2018)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
#         ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.2))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
        ax[(row,col)].axhline(0, lw=0.25, c='0.5')

ax[(0,0)].yaxis.set_major_locator(MultipleLocator(0.05))
ax[(0,1)].yaxis.set_major_locator(MultipleLocator(0.05))
ax[(0,2)].yaxis.set_major_locator(MultipleLocator(10))
ax[(0,3)].yaxis.set_major_locator(MultipleLocator(10))
ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0,))
ax[(0,1)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0,))

pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
    handles=patches, loc='upper right', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, 1-ax track – fixed', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]

Orientation optimization, fixed-tilt

In [35]:
########## Baseline = mustrun
### Data-indexed parameters
data = [
    'CF_OptRev-OptCF_hist,da,f,mustrun',
    'CF_OptRev-OptCF_hist,rt,f,mustrun',
    'CF_OptRev-OptCF_hist,da,f,curtail,baselinemustrun',
    'CF_OptRev-OptCF_hist,rt,f,curtail,baselinemustrun',
    'Rev_OptRev-OptCF_hist,da,f,mustrun',
    'Rev_OptRev-OptCF_hist,rt,f,mustrun',
    'Rev_OptRev-OptCF_hist,da,f,curtail,baselinemustrun',
    'Rev_OptRev-OptCF_hist,rt,f,curtail,baselinemustrun',
]
colindex = [0, 0, 1, 1, 2, 2, 3, 3,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',
             'left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],
         mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),
            slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))

### Column-indexed parameters
ylim = [
    [-0.09, 0.01],
    [-0.09, 0.01],
    [-1,20],
    [-1,20],
]
xlim = [
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
    [2009.4, 2018],
]

majlocs = [0.05, 0.05, 10, 10]
minlocs = [2, 2, 2, 2,]

ylabel = [
    'Capacity Factor',
    'Capacity Factor',
    'Revenue [$/kWac-yr]',
    'Revenue [$/kWac-yr]',
]

note = [
    '(must-run)',
    '(curtailable)',
    '(must-run)',
    '(curtailable)',
]
y1 = 1.2     # 1.2 if using note,  1 if no note
y2 = 1.07  # 1.07 if using note, 1.04 if no note

gridspec_kw = {'width_ratios': [2, 2, 2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])

### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey='col', gridspec_kw=gridspec_kw,
                    figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight), 
                   )
for row, iso in enumerate(isos):
    for datum in data:
        dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
                   .pivot(index='ISO:Node',columns='yearlmp',values=datum))

        pvvm.plots.plotquarthist(
            ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]], 
            density=True, bootstrap=bootstrap, 
            histcolor=color[datum], hist_range=ylim[colindex[datum]],
            direction=direction[datum], squeeze=squeeze[datum],
            quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
            histpad=(-0.15 if direction[datum] == 'left' else 0.15),
            format_axes=False,
        )

### Format axis
for row, iso in enumerate(isos):
    for col in range(ncols):
        ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
        ### x ticks
        ax[(row,col)].set_xticks([2010,2014])
        ax[(row,col)].set_xticklabels(
            ['2010','2014'], rotation=0, ha='center')
        ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
        ax[(row,col)].set_xlim(*xlim[col])
        ### Add title
        ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
        ### Add annotation
        ax[(0,col)].annotate(
            note[col], xy=(0.5,1.05), xycoords='axes fraction',
            ha='center', va='bottom', fontsize='large')

        ### Format axis
        ax[(row,col)].set_ylim(*ylim[col])
#         ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
        ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
        ax[(row,col)].axhline(0, lw=0.25, c='0.5')

ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
ax[(0,1)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
    
pvvm.plots.despine(ax)

### Legend
patches = [
    mpl.patches.Patch(
        facecolor=mc[market], edgecolor='none', 
        label=('Day-ahead' if market == 'da' else 'Real-time'))
    for market in ['da','rt']]
leg = ax[(-1,0)].legend(
    handles=patches, loc='lower left', frameon=False, ncol=2,
    columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)

# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, revenue-optimized – CF-optimized', weight='bold', y=y2, fontsize='xx-large')

plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide
  for i in range(len(binned_data_sets))]
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